CN115840800A - Patient information matching method, system, computer and readable storage medium - Google Patents
Patient information matching method, system, computer and readable storage medium Download PDFInfo
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Abstract
The invention provides a patient information matching method, a system, a computer and a readable storage medium, wherein the method comprises the following steps: retrieving inventory patient information similar to the actual patient information and identifying a plurality of contained data elements; sequentially carrying out forward maximum step size word segmentation and reverse maximum step size word segmentation on a first target data element in actual patient information and a second target data element in stock patient information, and respectively calculating a first matching degree value generated under the forward maximum step size word segmentation and a second matching degree value generated under the reverse maximum step size word segmentation of the first target data element and the second target data element; judging whether the larger value between the first matching degree value and the second matching degree value is larger than a preset threshold value or not; if yes, the existing patient information is judged to be matched with the actual patient information. By means of the method, stock patient information corresponding to the actual patient information received in real time can be accurately matched, and matching efficiency of the patient information is greatly improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a patient information matching method, a patient information matching system, a computer and a readable storage medium.
Background
The hospital is a medical institution which carries out necessary medical examination, treatment measures, nursing techniques, reception services, rehabilitation equipment, treatment and transportation and the like for patients according to laws, regulations and industrial specifications and mainly aims at curing and supporting injuries, and the service objects of the hospital not only comprise symptomatic patients and wounded persons, but also comprise the old who cannot take care of themselves or have limited activity and medical care dependence.
Most of the prior art applies an information query system, wherein most of the prior information query systems apply a patient master index matching strategy for matching required patient information in real time.
However, the existing matching strategy for patient master index mainly uses an equal matching method, that is, each data element of an incoming patient object is compared with the existing patient information one by one in an equal manner, if the comparison result reaches a certain threshold, the incoming patient object and the existing patient information are considered to belong to the same patient, otherwise, the incoming patient object is considered to be a new patient, so that the existing matching strategy for patient master index is only suitable for querying digital data elements, such as identity card numbers, mobile phone numbers and the like, but has poor effect when querying literal data elements such as names, home addresses, contact addresses, company names and the like, thereby being not beneficial to querying patient information.
Disclosure of Invention
Based on this, the present invention aims to provide a patient information matching method, system, computer and readable storage medium, so as to solve the problem that the patient master index matching strategy in the prior art is only suitable for querying digital data elements, such as identification numbers, mobile phone numbers, etc., but has a poor effect on querying textual data elements, such as names, home addresses, contact addresses, company names, etc., and is not beneficial to patient information query.
In a first aspect, an embodiment of the present invention provides a patient information matching method, where the method includes:
when actual patient information input by a user is received, searching stock patient information similar to the actual patient information in a preset database, and identifying a plurality of data elements respectively contained in the actual patient information and the stock patient information;
sequentially performing forward maximum step size word segmentation and reverse maximum step size word segmentation on a first target data element in the actual patient information and a second target data element in the stock patient information, and respectively calculating a first matching value generated under the forward maximum step size word segmentation and a second matching value generated under the reverse maximum step size word segmentation of the first target data element and the second target data element, wherein the first target data element corresponds to the second target data element;
judging whether the larger value between the first matching degree value and the second matching degree value is larger than a preset threshold value or not;
and if the larger value between the first matching degree value and the second matching degree value is larger than the preset threshold value, judging that the stock patient information is matched with the actual patient information.
The invention has the beneficial effects that: when actual patient information input by a user is received, stock patient information similar to the actual patient information is retrieved from a preset database, and a plurality of data elements respectively contained in the actual patient information and the stock patient information are identified; further, forward maximum step size word segmentation and reverse maximum step size word segmentation are sequentially carried out on a first target data element in actual patient information and a second target data element in stock patient information, and a first matching degree value generated by the first target data element and the second target data element under the forward maximum step size word segmentation and a second matching degree value generated by the second target data element under the reverse maximum step size word segmentation are respectively calculated; on the basis, whether the larger value between the first matching degree value and the second matching degree value is larger than a preset threshold value is judged; specifically, if yes, it is determined that the current stock patient information matches the actual patient information. By means of the method, stock patient information corresponding to the actual patient information received in real time can be accurately matched through the matching results of the forward and reverse maximum step length word segmentation, the matching efficiency of the patient information is greatly improved, the generation of redundant data is reduced, and the medical experience of the patient is correspondingly improved.
Preferably, the step of sequentially performing forward maximum step-size word segmentation and reverse maximum step-size word segmentation on the first target data element in the actual patient information and the second target data element in the inventory patient information includes:
when the forward maximum step-length word segmentation is performed on a first target data element in the actual patient information and a second target data element in the stock patient information, a first text string contained in the first target data element and a second text string contained in the second target data element are identified, and the first text string and the second text string are respectively scanned and segmented according to a first scanning sequence, so that the first text string is split into a plurality of first word groups, and the second text string is split into a plurality of second word groups.
Preferably, the step of calculating a first matching degree value generated by the first target data element and the second target data element under the forward maximum-step-size participle and a second matching degree value generated by the second target data element under the backward maximum-step-size participle respectively comprises:
respectively correcting the plurality of first phrases and the plurality of second phrases through a preset synonym library and a preset address library to respectively generate a plurality of corresponding first standard phrases and a plurality of corresponding second standard phrases;
identifying the total number of first characters contained in a plurality of first standard phrases, and detecting the number of first matched characters between the first standard phrases and the second standard phrases;
and calculating the first matching degree value according to the total number of the first characters and the number of the first matched characters.
Preferably, the step of sequentially performing forward maximum step-size word segmentation and reverse maximum step-size word segmentation on the first target data element in the actual patient information and the second target data element in the inventory patient information includes:
when the reverse maximum step-size word segmentation is performed on a first target data element in the actual patient information and a second target data element in the stock patient information, the first text string contained in the first target data element and the second text string contained in the second target data element are identified, and the first text string and the second text string are respectively scanned and segmented according to a second scanning sequence, so that the first text string is split into a plurality of third word groups, and the second text string is split into a plurality of fourth word groups.
Preferably, the step of calculating a first matching degree value generated by the first target data element and the second target data element under the forward maximum-step-size participle and a second matching degree value generated by the second target data element under the backward maximum-step-size participle respectively comprises:
respectively correcting the plurality of third phrases and the plurality of fourth phrases through the preset synonym library and the preset address library to respectively generate a plurality of corresponding third standard phrases and a plurality of corresponding fourth standard phrases;
identifying the total number of second characters contained in a plurality of third standard phrases, and detecting the number of second matched characters between the third standard phrases and the fourth standard phrases;
and calculating the second matching degree value according to the total number of the second characters and the number of the second matched characters.
Preferably, when the first target data element and the second target data element are both plural, the method further includes:
calculating a first matching degree mean value of the plurality of first target data elements sequentially generated under the forward maximum step-size word segmentation and the reverse maximum step-size word segmentation, and calculating a second matching degree mean value of the plurality of second target data elements sequentially generated under the forward maximum step-size word segmentation and the reverse maximum step-size word segmentation;
judging whether the first matching degree mean value is larger than the second matching degree mean value or not;
if the first matching degree mean value is larger than the second matching degree mean value, judging whether the first matching degree mean value is larger than the preset threshold value;
and if the first matching degree mean value is larger than the preset threshold value, judging that the stock patient information is matched with the actual patient information.
Preferably, after the step of determining that the stock patient information matches the actual patient information, the method further comprises:
comparing the stock patient information with the actual patient information, and detecting the patient information which is different between the stock patient information and the actual patient information;
judging whether the differentiated patient information is stored in the stock patient information;
and if the judgment result shows that the distinguishing patient information is not stored in the stock patient information, correspondingly updating the distinguishing patient information into the stock patient information so as to finish the real-time updating of the stock patient information.
A second aspect of an embodiment of the present invention provides a patient information matching system, including:
the receiving module is used for retrieving stock patient information similar to the actual patient information from a preset database when the actual patient information input by a user is received, and identifying a plurality of data elements respectively contained in the actual patient information and the stock patient information;
a calculation module, configured to perform forward maximum step-size word segmentation and reverse maximum step-size word segmentation on a first target data element in the actual patient information and a second target data element in the inventory patient information in sequence, and calculate a first matching value generated by the first target data element and the second target data element under the forward maximum step-size word segmentation and a second matching value generated by the second target data element under the reverse maximum step-size word segmentation, respectively, where the first target data element corresponds to the second target data element;
the judging module is used for judging whether the larger value between the first matching degree value and the second matching degree value is larger than a preset threshold value or not;
and the execution module is used for judging that the stock patient information is matched with the actual patient information if the larger value between the first matching degree value and the second matching degree value is larger than the preset threshold value.
In the patient information matching system, the calculation module is specifically configured to:
when the forward maximum step-length word segmentation is performed on a first target data element in the actual patient information and a second target data element in the stock patient information, a first text string contained in the first target data element and a second text string contained in the second target data element are identified, and the first text string and the second text string are respectively scanned and segmented according to a first scanning sequence, so that the first text string is split into a plurality of first word groups, and the second text string is split into a plurality of second word groups.
In the above patient information matching system, the calculation module is further specifically configured to:
respectively correcting the plurality of first phrases and the plurality of second phrases through a preset synonym library and a preset address library to respectively generate a plurality of corresponding first standard phrases and a plurality of corresponding second standard phrases;
identifying the total number of first characters contained in a plurality of first standard phrases, and detecting the number of first matched characters between the first standard phrases and the second standard phrases;
and calculating the first matching degree value according to the total number of the first characters and the number of the first matched characters.
In the above patient information matching system, the calculation module is further specifically configured to:
when the reverse maximum step-size word segmentation is performed on a first target data element in the actual patient information and a second target data element in the stock patient information, the first text string contained in the first target data element and the second text string contained in the second target data element are identified, and the first text string and the second text string are respectively scanned and segmented according to a second scanning sequence, so that the first text string is split into a plurality of third word groups, and the second text string is split into a plurality of fourth word groups.
In the above patient information matching system, the calculation module is further specifically configured to:
respectively correcting the plurality of third phrases and the plurality of fourth phrases through the preset synonym library and the preset address library to respectively generate a plurality of corresponding third standard phrases and a plurality of corresponding fourth standard phrases;
identifying the total number of second characters contained in a plurality of third standard phrases, and detecting the number of second matched characters between the third standard phrases and the fourth standard phrases;
and calculating the second matching degree value according to the total number of the second characters and the number of the second matched characters.
In the above patient information matching system, when the first target data element and the second target data element are both multiple, the patient information matching system further includes a processing module, and the processing module is specifically configured to:
calculating a first matching degree mean value of the plurality of first target data elements sequentially generated under the forward maximum step-size word segmentation and the reverse maximum step-size word segmentation, and calculating a second matching degree mean value of the plurality of second target data elements sequentially generated under the forward maximum step-size word segmentation and the reverse maximum step-size word segmentation;
judging whether the first matching degree mean value is larger than the second matching degree mean value or not;
if the first matching degree mean value is larger than the second matching degree mean value, judging whether the first matching degree mean value is larger than the preset threshold value;
and if the first matching degree mean value is larger than the preset threshold value, judging that the stock patient information is matched with the actual patient information.
In the above patient information matching system, the patient information matching system further includes an update module, and the update module is specifically configured to:
comparing the stock patient information with the actual patient information, and detecting the patient information which is different between the stock patient information and the actual patient information;
judging whether the differentiated patient information is stored in the stock patient information;
and if the judgment result shows that the distinguishing patient information is not stored in the stock patient information, correspondingly updating the distinguishing patient information into the stock patient information so as to finish the real-time updating of the stock patient information.
A third aspect of embodiments of the present invention provides a computer, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the patient information matching method as described above when executing the computer program.
A fourth aspect of embodiments of the present invention proposes a readable storage medium on which a computer program is stored, which when executed by a processor, implements a patient information matching method as described above.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
Fig. 1 is a flowchart of a patient information matching method according to a first embodiment of the present invention;
fig. 2 is a block diagram of a patient information matching system according to a sixth embodiment of the present invention.
The following detailed description will further illustrate the invention in conjunction with the above-described figures.
Detailed Description
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Several embodiments of the invention are presented in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like as used herein are for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
The existing patient main index matching strategy mainly uses an equal matching method, namely, each data element of an incoming patient object is compared with the existing patient information one by one in an equal manner, if the comparison result reaches a certain threshold value, the incoming patient object and the existing patient information are considered to belong to the same patient, otherwise, the incoming patient object is considered to be a new patient, so that the existing patient main index matching strategy is only suitable for inquiring digital data elements, such as identity card numbers, mobile phone numbers and the like, but the effect is poor when inquiring text data elements such as names, home addresses, contact addresses, company names and the like, and the inquiry of the patient information is not facilitated.
Referring to fig. 1, a patient information matching method according to a first embodiment of the present invention is shown, and the patient information matching method according to this embodiment can accurately match stock patient information corresponding to actual patient information received in real time according to matching results of forward and reverse maximum step-size word segmentation, thereby greatly improving matching efficiency of patient information, reducing generation of redundant data, and correspondingly improving medical experience of a patient.
Specifically, the patient information matching method provided in this embodiment specifically includes the following steps:
step S10, when actual patient information input by a user is received, stock patient information similar to the actual patient information is searched in a preset database, and a plurality of data elements respectively contained in the actual patient information and the stock patient information are identified;
specifically, in this embodiment, it should be noted that the patient information matching method provided in this embodiment is specifically applied to patient information query systems of various medical institutions, and is used for matching needed patient information simply and quickly in real time.
In addition, in this embodiment, it should be noted that a database is preset in the patient information query system of each medical institution for storing the treatment information of the treated patient, that is, a large amount of stock patient information is stored in the database, and the stock patient information is the existing patient information.
Therefore, in this step, it should be noted that, when the actual patient information input by the user is received in real time, specifically, the actual patient information may be one or more of "name", "home address", and "phone number" input by the current user, on the basis of which, this step further initially searches the preset database for the patient information inventory similar to the actual patient information currently received in real time. It should be noted that the patient information in stock may be information that has been recorded by the current user before, or may be patient information of other users, and therefore, it is necessary to further determine whether the current patient information in stock matches the actual patient information input by the current user in real time.
Furthermore, this step also identifies a plurality of data elements respectively included in the current actual patient information and the current inventory patient information, specifically, the plurality of data elements may be data such as "home address", "patient name", and "phone number".
Step S20, sequentially performing forward maximum step size word segmentation and reverse maximum step size word segmentation on a first target data element in the actual patient information and a second target data element in the stock patient information, and respectively calculating a first matching value generated under the forward maximum step size word segmentation and a second matching value generated under the reverse maximum step size word segmentation of the first target data element and the second target data element, wherein the first target data element corresponds to the second target data element;
furthermore, in this step, it should be noted that, in order to accurately determine whether the current actual patient information matches the current stock patient information, this step further selects a first target data element from the current actual patient information, and correspondingly selects a second target data element from the current stock patient information, specifically, the first target data element corresponds to the second target data element, that is, both the first target data element and the second target data element are data elements of the same type, for example, both the first target data element and the second target data element are "home address" data elements or "disease description" data elements.
Furthermore, this step sequentially performs forward maximum step size word segmentation and reverse maximum step size word segmentation on a first target data element in the current actual patient information and a second target data element in the current inventory patient information, and at the same time, calculates a first matching degree value generated by the current first target data element and the current second target data element under the forward maximum step size word segmentation and a corresponding second matching degree value generated under the reverse maximum step size word segmentation, where the first matching degree value and the second matching degree value are both specific numerical values.
Step S30, judging whether the larger value between the first matching degree value and the second matching degree value is larger than a preset threshold value or not;
specifically, this step may further determine whether a larger value between the current first matching value and the current second matching value is greater than a preset threshold, for example, when the first matching value is greater than the second matching value, the current first matching value is compared with the preset threshold, and correspondingly, when the second matching value is greater than the first matching value, the current second matching value is compared with the preset threshold.
And step S40, if the larger value between the first matching degree value and the second matching degree value is judged to be larger than the preset threshold value, judging that the stock patient information is matched with the actual patient information.
Finally, in this embodiment, it should be noted that, if it is determined in real time that the larger value between the current first matching value and the current second matching value is greater than the preset threshold in this step, it is determined that the current inventory patient information matches the current actual patient information, and correspondingly, if it is determined in real time that the larger value between the current first matching value and the current second matching value is smaller than the preset threshold, the steps S10 to S30 need to be repeated until it is determined that the larger value between the first matching value and the second matching value is greater than the preset threshold.
When the system is used, when actual patient information input by a user is received, stock patient information similar to the actual patient information is searched in a preset database, and a plurality of data elements respectively contained in the actual patient information and the stock patient information are identified; further, forward maximum step size word segmentation and reverse maximum step size word segmentation are sequentially carried out on a first target data element in actual patient information and a second target data element in stock patient information, and a first matching degree value generated by the first target data element and the second target data element under the forward maximum step size word segmentation and a second matching degree value generated by the second target data element under the reverse maximum step size word segmentation are respectively calculated; on the basis, whether the larger value between the first matching degree value and the second matching degree value is larger than a preset threshold value is judged; specifically, if yes, it is determined that the current stock patient information matches the actual patient information. By means of the method, stock patient information corresponding to the actual patient information received in real time can be accurately matched through the matching results of the forward and reverse maximum step length word segmentation, the matching efficiency of the patient information is greatly improved, the generation of redundant data is reduced, and the medical experience of the patient is correspondingly improved.
It should be noted that the above implementation process is only for illustrating the applicability of the present application, but this does not represent that the patient information matching method of the present application has only the above-mentioned implementation flow, and on the contrary, the patient information matching method of the present application can be incorporated into the feasible embodiments of the present application as long as the patient information matching method of the present application can be implemented.
In summary, the patient information matching method provided by the embodiment of the invention can accurately match the stock patient information corresponding to the actual patient information received in real time through the matching results of the forward and reverse maximum step length word segmentation, thereby greatly improving the matching efficiency of the patient information, reducing the generation of redundant data, and correspondingly improving the medical experience of the patient.
A second embodiment of the present invention also provides a patient information matching method, and the patient information matching method provided in this embodiment is different from the patient information matching method provided in the first embodiment in that:
specifically, in this embodiment, it should be noted that the step of sequentially performing forward maximum-step-size word segmentation and reverse maximum-step-size word segmentation on the first target data element in the actual patient information and the second target data element in the stock patient information includes:
when the forward maximum step-length word segmentation is performed on a first target data element in the actual patient information and a second target data element in the stock patient information, a first text string contained in the first target data element and a second text string contained in the second target data element are identified, and the first text string and the second text string are scanned and segmented according to a first scanning sequence, so that the first text string is split into a plurality of first phrases, and the second text string is split into a plurality of second phrases.
Specifically, in this embodiment, it should be noted that, when the forward maximum step size word segmentation needs to be performed on a first target data element in the current actual patient information and a second target data element in the current inventory patient information, in this embodiment, a first text string included in the current first target data element is first identified, and correspondingly, a second text string included in the current second target data element is identified, and the current first text string and the second text string are scanned and segmented according to a left-to-right sequence, so as to correspondingly split the current first text string into a plurality of first word groups, and split the current second text string into a plurality of second word groups.
Further, in this embodiment, for convenience of understanding, it is to be noted that, for example, the present embodiment takes a "home address" data element as an example for explanation, specifically, for example, a first text string in a first target data element included in the actual patient information acquired in the present embodiment is "building No. 5F of no-tin new wu district, lake dagu 200F of wu district, jiangsu province", and correspondingly, a second text string in a second target data element included in the patient information acquired in the present embodiment is "building No. 5 of no-tin new district, lake dagu 200 innovative garden F of no-tin new district, lake dao 200), on the basis, the current first text string and the second text string are scanned and participled from left to right in sequence, so that the forward maximum step-length participle result obtained for the first text string is 'Jiangsu province/Wuxi city/New Wu district/Trapa avenue/200/number/F/district/5/number building', and correspondingly, the forward maximum step-length participle result obtained for the second text string is 'Wuxi/New district/Trapa avenue/200/number/creative garden/F/district/5/number building'.
In this embodiment, it should be noted that the step of calculating the first matching degree values generated by the first target data element and the second target data element under the forward maximum-step participle includes:
respectively correcting the plurality of first phrases and the plurality of second phrases through a preset synonym library and a preset address library to respectively generate a plurality of corresponding first standard phrases and a plurality of corresponding second standard phrases;
identifying the total number of first characters contained in a plurality of first standard phrases, and detecting the number of first matched characters between the first standard phrases and the second standard phrases;
and calculating the first matching degree value according to the total number of the first characters and the number of the first matched characters.
Further, in this embodiment, it should be noted that, after the first phrase and the second phrase are sequentially obtained through the above steps, in order to further improve the matching accuracy, the present embodiment further performs correction processing on the current plurality of first phrases and the current plurality of second phrases respectively through a preset synonym library and a preset address library. Taking the address data element as an example, the original address information may lack information such as administrative levels, roads, residential quarters and the like, so that after the word group is corrected by combining the synonym library and the address library, the missing information such as the administrative levels, the roads, the residential quarters and the like can be supplemented, thereby improving the matching rate of the data element and further achieving the purpose of prompting the matching rate of the patient information.
Specifically, in this embodiment, the synonym library and the address library are further used to perform omission-checking and gap-filling on phrases appearing in the current first phrase and the current second phrase, for example, when the forward maximum step-size segmentation result obtained for the second text string is "tin-free/new district/lake avenue/200/number/creation garden/F/district/5/number building", on this basis, the embodiment corrects the forward maximum step-size segmentation result of the current second text string into "Jiangsu province/tin-free city/new district/lake avenue/200/number/creation garden/F/district/5/number building" through the synonym library and the address library, so that the corresponding first standard phrase and the corresponding second standard phrase can be generated to improve the subsequent calculation accuracy.
Furthermore, the present embodiment may further identify a total number of first characters included in the current first standard phrase, and at the same time, the present embodiment further detects a first number of matched characters between the current first standard phrase and the current second standard phrase, and finally, the required first matching value may be calculated only according to the obtained total number of first characters and the first number of matched characters.
Specifically, in this embodiment, for convenience of understanding, it is to be noted that, for example, the modified first standard phrase is "Jiangsu province/Wuxi city/New Wu district/Trapa dao/200/number/F/district/5/number building", and correspondingly, the modified second standard phrase is "Jiangsu province/Wuxi city/New district/Trapa dao/200/number/Innovation garden/F/district/5/number building", where it is to be noted that generally, the role of a word segmentation is limited, which may affect the matching effect, so that the present embodiment only counts the word segments of more than one word. Therefore, the total number of the first characters calculated by the embodiment is 22, and correspondingly, the number of the first matching characters calculated by the embodiment between the first standard phrase and the second standard phrase is 17, and the specific numerical value of the first matching degree calculated by the embodiment is 77.3%.
It should be noted that, the method provided by the second embodiment of the present invention, which implements the same principle and produces some technical effects as the first embodiment, may refer to the corresponding contents provided by the first embodiment for the sake of brief description, where this embodiment is not mentioned.
In summary, the patient information matching method provided by the embodiment of the invention can accurately match the stock patient information corresponding to the actual patient information received in real time through the matching results of the forward and reverse maximum step length word segmentation, thereby greatly improving the matching efficiency of the patient information, reducing the generation of redundant data, and correspondingly improving the medical experience of the patient.
A third embodiment of the present invention also provides a patient information matching method, and the patient information matching method provided in this embodiment is different from the patient information matching method provided in the first embodiment in that:
specifically, in this embodiment, it should be noted that the step of sequentially performing forward maximum-step-size word segmentation and reverse maximum-step-size word segmentation on the first target data element in the actual patient information and the second target data element in the stock patient information includes:
when the reverse maximum step-size word segmentation is performed on a first target data element in the actual patient information and a second target data element in the stock patient information, the first text string contained in the first target data element and the second text string contained in the second target data element are identified, and the first text string and the second text string are respectively scanned and segmented according to a second scanning sequence, so that the first text string is split into a plurality of third word groups, and the second text string is split into a plurality of fourth word groups.
Similarly, in this embodiment, it should be noted that when reverse maximum step size word segmentation needs to be performed on a first target data element in the actual patient information and a second target data element in the stock patient information, this embodiment also identifies a first text string included in a current first target data element and a second text string included in a second target data element, and on this basis, scans and segments the first text string and the second text string in a right-to-left manner to correspondingly split the current first text string into a plurality of third word groups and split the current second text string into a plurality of fourth word groups.
Specifically, for example, the third phrase obtained in this embodiment is "jiangsu province/tin-free city/new wu district/great path of lake diamond/200 # region/F district/5 # floor", and the corresponding fourth phrase obtained is "tin-free/new district/great path of lake diamond/200 # region/innovation garden/F district/5 # floor", so as to perform subsequent calculation.
In this embodiment, it should be noted that the step of calculating a first matching degree value generated by the first target data element and the second target data element under the forward maximum-step-size participle and a second matching degree value generated by the second target data element under the backward maximum-step-size participle respectively includes:
respectively correcting the plurality of third phrases and the plurality of fourth phrases through the preset synonym library and the preset address library to respectively generate a plurality of corresponding third standard phrases and a plurality of corresponding fourth standard phrases;
identifying the total number of second characters contained in a plurality of third standard phrases, and detecting the number of second matched characters between the third standard phrases and the fourth standard phrases;
and calculating the second matching degree value according to the total number of the second characters and the number of the second matched characters.
Furthermore, in this embodiment, it should be noted that, similarly, in this embodiment, the current third phrase and fourth phrase are modified through the synonym library and the address library to generate corresponding third standard phrase and fourth standard phrase. Specifically, for example, the third standard phrase obtained in this embodiment is "Jiangsu province/Wuxi city/New Wu district/Trapa great road/No. 200/F district/No. 5 building", and the corresponding fourth standard phrase obtained is "Jiangsu province/Wuxi city/New district/Trapa great road/No. 200/Innovation garden/F district/No. 5 building".
On the basis, the present embodiment further identifies that the total number of the second characters included in the current third standard phrase is 22, and correspondingly, if the present embodiment detects that the number of the second matched characters between the current third standard phrase and the current fourth standard phrase is 21, the second matching degree calculated according to the total number of the current second characters and the number of the second matched characters is 95.5%.
It should be noted that, the method provided by the third embodiment of the present invention, which implements the same principle and produces some technical effects as the first embodiment, may refer to the corresponding contents provided by the first embodiment for the sake of brief description, where this embodiment is not mentioned.
In summary, the patient information matching method provided by the embodiment of the invention can accurately match the stock patient information corresponding to the actual patient information received in real time through the matching results of the forward and reverse maximum step length word segmentation, thereby greatly improving the matching efficiency of the patient information, reducing the generation of redundant data, and correspondingly improving the medical experience of the patient.
A fourth embodiment of the present invention also provides a patient information matching method, and the patient information matching method provided in this embodiment is different from the patient information matching method provided in the first embodiment in that:
specifically, in this embodiment, it should be noted that, when a plurality of first target data elements and a plurality of second target data elements are both provided, the method further includes:
calculating a first matching degree mean value of a plurality of first target data elements sequentially generated under the forward maximum step-size word segmentation and the reverse maximum step-size word segmentation, and calculating a second matching degree mean value of a plurality of second target data elements sequentially generated under the forward maximum step-size word segmentation and the reverse maximum step-size word segmentation;
judging whether the first matching degree mean value is larger than the second matching degree mean value or not;
if the first matching degree mean value is larger than the second matching degree mean value, judging whether the first matching degree mean value is larger than the preset threshold value;
and if the first matching degree mean value is larger than the preset threshold value, judging that the stock patient information is matched with the actual patient information.
Specifically, in this embodiment, it should be noted that, when a plurality of first target data elements and a plurality of second target data elements need to be compared at the same time, the present embodiment sequentially calculates a first matching degree mean value generated by the current plurality of first target data elements sequentially under the forward maximum step-size participle and the reverse maximum step-size participle through the above-mentioned manner, and at the same time, correspondingly calculates a second matching degree mean value generated by the current plurality of second target data elements under the forward maximum step-size participle and the reverse maximum step-size participle, on the basis, further determines whether the current first matching degree mean value is greater than the current second matching degree mean value, specifically, if it is determined that the current first matching degree mean value is greater than the current second matching degree mean value, determines whether the current first matching degree mean value is greater than the preset threshold; further, if the current first matching degree mean value is judged to be larger than the preset threshold value, the inventory patient information is judged to be matched with the actual patient information.
It should be noted that, the method provided by the fourth embodiment of the present invention, which implements the same principle and produces some technical effects as the first embodiment, for brevity, the corresponding contents provided by the first embodiment may be referred to where this embodiment is not mentioned.
In summary, the patient information matching method provided by the embodiment of the invention can accurately match the stock patient information corresponding to the actual patient information received in real time through the matching results of the forward and reverse maximum step length word segmentation, thereby greatly improving the matching efficiency of the patient information, reducing the generation of redundant data, and correspondingly improving the medical experience of the patient.
A fifth embodiment of the present invention also provides a patient information matching method, and the patient information matching method provided in this embodiment is different from the patient information matching method provided in the first embodiment in that:
specifically, in this embodiment, after the step of determining that the stock patient information matches the actual patient information, the method further includes:
comparing the stock patient information with the actual patient information, and detecting the patient information which is different between the stock patient information and the actual patient information;
judging whether the differentiated patient information is stored in the stock patient information;
and if the judgment result shows that the distinguishing patient information is not stored in the stock patient information, correspondingly updating the distinguishing patient information into the stock patient information so as to finish the real-time updating of the stock patient information.
Specifically, in this embodiment, in order to update the patient information in the stock in the database in real time, the embodiment compares the actual patient information received in real time with the stored patient information in stock, at the same time, detects the patient information that is distinguished between the current patient information in stock and the current actual patient information, and further determines whether the current patient information in stock is stored, specifically, if it is determined in real time that the current patient information in stock is not stored, the current patient information in stock needs to be updated to the current patient information in stock, so as to complete the real-time update of the current patient information in stock.
Correspondingly, if the current patient information is judged to be stored in the current stock patient information in real time, the current stock patient information does not need to be updated.
It should be noted that, the method provided by the fifth embodiment of the present invention, which implements the same principle and produces some technical effects as the first embodiment, may refer to the corresponding contents provided by the first embodiment for the sake of brief description, where this embodiment is not mentioned.
In summary, the patient information matching method provided by the embodiment of the invention can accurately match the stock patient information corresponding to the actual patient information received in real time through the matching results of the forward and reverse maximum step length word segmentation, thereby greatly improving the matching efficiency of the patient information, reducing the generation of redundant data, and correspondingly improving the medical experience of the patient.
Referring to fig. 2, a patient information matching system according to a sixth embodiment of the present invention is shown, the system including:
the receiving module 12 is configured to, when actual patient information input by a user is received, retrieve stock patient information similar to the actual patient information from a preset database, and identify a plurality of data elements respectively included in the actual patient information and the stock patient information;
a calculating module 22, configured to perform forward maximum step size word segmentation and reverse maximum step size word segmentation on a first target data element in the actual patient information and a second target data element in the stock patient information in sequence, and calculate a first matching value generated by the first target data element and the second target data element under the forward maximum step size word segmentation and a second matching value generated by the second target data element under the reverse maximum step size word segmentation, respectively, where the first target data element corresponds to the second target data element;
a determining module 32, configured to determine whether a larger value between the first matching degree value and the second matching degree value is greater than a preset threshold;
and the executing module 42 is configured to determine that the stock patient information matches with the actual patient information if it is determined that the larger value of the first matching degree value and the second matching degree value is greater than the preset threshold.
In the patient information matching system, the calculation module 22 is specifically configured to:
when the forward maximum step-length word segmentation is performed on a first target data element in the actual patient information and a second target data element in the stock patient information, a first text string contained in the first target data element and a second text string contained in the second target data element are identified, and the first text string and the second text string are respectively scanned and segmented according to a first scanning sequence, so that the first text string is split into a plurality of first word groups, and the second text string is split into a plurality of second word groups.
In the patient information matching system, the calculation module 22 is further specifically configured to:
respectively correcting the plurality of first phrases and the plurality of second phrases through a preset synonym library and a preset address library to respectively generate a plurality of corresponding first standard phrases and a plurality of corresponding second standard phrases;
identifying the total number of first characters contained in a plurality of first standard phrases, and detecting the number of first matched characters between the first standard phrases and the second standard phrases;
and calculating the first matching degree value according to the total number of the first characters and the number of the first matched characters.
In the patient information matching system, the calculation module 22 is further specifically configured to:
when the reverse maximum step-size word segmentation is performed on a first target data element in the actual patient information and a second target data element in the stock patient information, the first text string contained in the first target data element and the second text string contained in the second target data element are identified, and the first text string and the second text string are respectively scanned and segmented according to a second scanning sequence, so that the first text string is split into a plurality of third word groups, and the second text string is split into a plurality of fourth word groups.
In the patient information matching system, the calculation module 22 is further specifically configured to:
respectively correcting the plurality of third phrases and the plurality of fourth phrases through the preset synonym library and the preset address library to respectively generate a plurality of corresponding third standard phrases and a plurality of corresponding fourth standard phrases;
identifying the total number of second characters contained in a plurality of third standard phrases, and detecting the number of second matched characters between the third standard phrases and the fourth standard phrases;
and calculating the second matching degree value according to the total number of the second characters and the number of the second matched characters.
In the above patient information matching system, when the first target data element and the second target data element are both multiple, the patient information matching system further includes a processing module 52, and the processing module 52 is specifically configured to:
calculating a first matching degree mean value of the plurality of first target data elements sequentially generated under the forward maximum step-size word segmentation and the reverse maximum step-size word segmentation, and calculating a second matching degree mean value of the plurality of second target data elements sequentially generated under the forward maximum step-size word segmentation and the reverse maximum step-size word segmentation;
judging whether the first matching degree mean value is larger than the second matching degree mean value or not;
if the first matching degree mean value is larger than the second matching degree mean value, judging whether the first matching degree mean value is larger than the preset threshold value;
and if the first matching degree mean value is larger than the preset threshold value, judging that the stock patient information is matched with the actual patient information.
In the above patient information matching system, the patient information matching system further includes an updating module 62, and the updating module 62 is specifically configured to:
comparing the stock patient information with the actual patient information, and detecting the patient information which is different between the stock patient information and the actual patient information;
judging whether the patient distinguishing information is stored in the stock patient information;
and if the judgment result shows that the distinguishing patient information is not stored in the stock patient information, correspondingly updating the distinguishing patient information into the stock patient information so as to finish the real-time updating of the stock patient information.
A seventh embodiment of the present invention provides a computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the patient information matching method provided in the above embodiment.
An eighth embodiment of the present invention provides a readable storage medium, on which a computer program is stored, which when executed by a processor, implements the patient information matching method as provided in the above embodiments.
In summary, the patient information matching method, the patient information matching system, the computer and the readable storage medium provided by the embodiments of the present invention can accurately match the stock patient information corresponding to the actual patient information received in real time according to the matching results of the forward and reverse maximum step length word segmentation, thereby greatly improving the matching efficiency of the patient information, reducing the generation of redundant data, and correspondingly improving the medical experience of the patient.
The above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the modules may be located in the same processor; or the modules can be respectively positioned in different processors in any combination.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A patient information matching method, the method comprising:
when actual patient information input by a user is received, searching stock patient information similar to the actual patient information in a preset database, and identifying a plurality of data elements respectively contained in the actual patient information and the stock patient information;
sequentially performing forward maximum step size word segmentation and reverse maximum step size word segmentation on a first target data element in the actual patient information and a second target data element in the stock patient information, and respectively calculating a first matching value generated under the forward maximum step size word segmentation and a second matching value generated under the reverse maximum step size word segmentation of the first target data element and the second target data element, wherein the first target data element corresponds to the second target data element;
judging whether the larger value between the first matching degree value and the second matching degree value is larger than a preset threshold value or not;
and if the larger value between the first matching degree value and the second matching degree value is larger than the preset threshold value, judging that the stock patient information is matched with the actual patient information.
2. The patient information matching method according to claim 1, characterized in that: the step of sequentially performing forward maximum step size word segmentation and reverse maximum step size word segmentation on the first target data element in the actual patient information and the second target data element in the stock patient information comprises the following steps:
when the forward maximum step-length word segmentation is performed on a first target data element in the actual patient information and a second target data element in the stock patient information, a first text string contained in the first target data element and a second text string contained in the second target data element are identified, and the first text string and the second text string are respectively scanned and segmented according to a first scanning sequence, so that the first text string is split into a plurality of first word groups, and the second text string is split into a plurality of second word groups.
3. The patient information matching method according to claim 2, characterized in that: the step of calculating a first matching degree value generated by the first target data element and the second target data element under the forward maximum step-size participle and a second matching degree value generated under the reverse maximum step-size participle respectively comprises:
respectively correcting the plurality of first phrases and the plurality of second phrases through a preset synonym library and a preset address library to respectively generate a plurality of corresponding first standard phrases and a plurality of corresponding second standard phrases;
identifying the total number of first characters contained in a plurality of first standard phrases, and detecting the number of first matched characters between the first standard phrases and the second standard phrases;
and calculating the first matching degree value according to the total number of the first characters and the number of the first matched characters.
4. The patient information matching method according to claim 3, characterized in that: the step of sequentially performing forward maximum step size word segmentation and reverse maximum step size word segmentation on the first target data element in the actual patient information and the second target data element in the stock patient information comprises the following steps:
when the reverse maximum step-size word segmentation is performed on a first target data element in the actual patient information and a second target data element in the stock patient information, the first text string contained in the first target data element and the second text string contained in the second target data element are identified, and the first text string and the second text string are respectively scanned and segmented according to a second scanning sequence, so that the first text string is split into a plurality of third word groups, and the second text string is split into a plurality of fourth word groups.
5. The patient information matching method according to claim 4, characterized in that: the step of calculating a first matching degree value generated by the first target data element and the second target data element under the forward maximum step-size word segmentation and a second matching degree value generated by the second target data element under the reverse maximum step-size word segmentation respectively comprises the following steps:
respectively correcting the plurality of third phrases and the plurality of fourth phrases through the preset synonym library and the preset address library to respectively generate a plurality of corresponding third standard phrases and a plurality of corresponding fourth standard phrases;
identifying the total number of second characters contained in a plurality of third standard phrases, and detecting the number of second matched characters between the third standard phrases and the fourth standard phrases;
and calculating the second matching degree value according to the total number of the second characters and the number of the second matched characters.
6. The patient information matching method according to claim 1, characterized in that: when the first target data element and the second target data element are both plural, the method further comprises:
calculating a first matching degree mean value of the plurality of first target data elements sequentially generated under the forward maximum step-size word segmentation and the reverse maximum step-size word segmentation, and calculating a second matching degree mean value of the plurality of second target data elements sequentially generated under the forward maximum step-size word segmentation and the reverse maximum step-size word segmentation;
judging whether the first matching degree mean value is larger than the second matching degree mean value or not;
if the first matching degree average value is larger than the second matching degree average value, judging whether the first matching degree average value is larger than the preset threshold value;
and if the first matching degree mean value is larger than the preset threshold value, judging that the stock patient information is matched with the actual patient information.
7. The patient information matching method according to claim 1, characterized in that: after the step of determining that the stock patient information matches the actual patient information, the method further comprises:
comparing the stock patient information with the actual patient information, and detecting the patient information which is different between the stock patient information and the actual patient information;
judging whether the differentiated patient information is stored in the stock patient information;
and if the judgment result shows that the distinguishing patient information is not stored in the stock patient information, correspondingly updating the distinguishing patient information into the stock patient information so as to finish the real-time updating of the stock patient information.
8. A patient information matching system, the system comprising:
the receiving module is used for retrieving stock patient information similar to the actual patient information from a preset database when the actual patient information input by a user is received, and identifying a plurality of data elements respectively contained in the actual patient information and the stock patient information;
a calculation module, configured to perform forward maximum step size word segmentation and reverse maximum step size word segmentation on a first target data element in the actual patient information and a second target data element in the stock patient information in sequence, and calculate a first matching value generated by the first target data element and the second target data element under the forward maximum step size word segmentation and a second matching value generated by the second target data element under the reverse maximum step size word segmentation, respectively, where the first target data element corresponds to the second target data element;
the judging module is used for judging whether the larger value between the first matching degree value and the second matching degree value is larger than a preset threshold value or not;
and the execution module is used for judging that the stock patient information is matched with the actual patient information if the larger value between the first matching degree value and the second matching degree value is larger than the preset threshold value.
9. A computer comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the patient information matching method according to any one of claims 1 to 7 when executing the computer program.
10. A readable storage medium on which a computer program is stored which, when being executed by a processor, carries out a patient information matching method according to any one of claims 1 to 7.
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Inventor after: Xue Hao Inventor after: He Changhai Inventor after: Zeng Zhongan Inventor after: Fan Haidong Inventor after: Ye Kai Inventor after: Ding Chuan Inventor after: Lu Bingqing Inventor before: Xue Hao Inventor before: Ji Zhengrong Inventor before: He Changhai Inventor before: Zeng Zhongan Inventor before: Fan Haidong Inventor before: Ye Kai Inventor before: Ding Chuan Inventor before: Lu Bingqing |