CN112863694A - Information recommendation method and device - Google Patents
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- 208000024891 symptom Diseases 0.000 claims abstract description 194
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- 238000000605 extraction Methods 0.000 claims description 6
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- 208000013738 Sleep Initiation and Maintenance disease Diseases 0.000 description 8
- 206010022437 insomnia Diseases 0.000 description 8
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- 235000007164 Oryza sativa Nutrition 0.000 description 4
- 206010062519 Poor quality sleep Diseases 0.000 description 4
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- 238000005516 engineering process Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 244000144730 Amygdalus persica Species 0.000 description 1
- 240000007371 Cuscuta campestris Species 0.000 description 1
- 235000014066 European mistletoe Nutrition 0.000 description 1
- 241000488974 Loranthus Species 0.000 description 1
- 240000000249 Morus alba Species 0.000 description 1
- 235000008708 Morus alba Nutrition 0.000 description 1
- 235000006040 Prunus persica var persica Nutrition 0.000 description 1
- 244000152640 Rhipsalis cassutha Species 0.000 description 1
- 235000012300 Rhipsalis cassutha Nutrition 0.000 description 1
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- G16H70/00—ICT specially adapted for the handling or processing of medical references
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Abstract
The application provides an information recommendation method and device. The method comprises the following steps: acquiring diagnosis information input by a diagnosis and treatment system and a user identifier; acquiring a symptom name in the diagnosis information; acquiring a recommendation information base corresponding to the user identification; the recommendation information base comprises a corresponding relation between symptom names and medicine names; matching the symptom names in the recommendation information base; and acquiring the medicine name corresponding to the matched symptom name, and outputting the medicine name to the diagnosis and treatment system. The method can accurately recommend the medicine information.
Description
Technical Field
The present invention relates to the field of information technologies, and in particular, to an information recommendation method and apparatus.
Background
The traditional Chinese medicine diagnosis and treatment has the advantages of low price and small side effect. But traditional chinese medicine also has stored problems at the physician level.
In addition, the traditional Chinese medicine diagnosis and treatment also has the characteristics of large quantity of medicines and different medication habits of doctors, and a method for recommending relevant medication based on the medication habits of doctors and the symptoms of patients is urgently needed to be standardized to assist the traditional Chinese medicine diagnosis and treatment.
Disclosure of Invention
In view of this, the present application provides an information recommendation method and apparatus, which can accurately recommend medicine information.
In order to solve the technical problem, the technical scheme of the application is realized as follows:
in one embodiment, there is provided an information recommendation method, the method including:
acquiring diagnosis information input by a diagnosis and treatment system and a user identifier;
acquiring a symptom name in the diagnosis information;
acquiring a recommendation information base corresponding to the user identification; the recommendation information base comprises a corresponding relation between symptom names and medicine names;
matching the symptom names in the recommendation information base;
and acquiring the medicine name corresponding to the matched symptom name, and outputting the medicine name to the diagnosis and treatment system.
In another embodiment, there is provided an information recommendation apparatus including: the device comprises an establishing unit, a first acquiring unit, a second acquiring unit, a matching unit, a third acquiring unit and an output unit;
the establishing unit is used for storing the corresponding relation between the user identification and the recommendation information base;
the first acquisition unit is used for acquiring diagnosis information input by the diagnosis and treatment system and a user identifier; acquiring a symptom name in the diagnosis information;
the second obtaining unit is used for obtaining the recommendation information base corresponding to the user identifier obtained by the first obtaining unit in the establishing unit; the recommendation information base comprises a corresponding relation between symptom names and medicine names;
the matching unit is used for matching the symptom name acquired by the first acquiring unit in the recommendation information base acquired by the second acquiring unit;
the third obtaining unit is used for obtaining the medicine name corresponding to the symptom name matched by the matching unit;
the output unit is used for outputting the medicine name acquired by the third acquisition unit to the diagnosis and treatment system.
In another embodiment, an electronic device is provided, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the information recommendation method when executing the program.
In another embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the information recommendation method.
According to the technical scheme, when the diagnosis information and the user identification in the diagnosis and treatment system are obtained, the medicine name is recommended for the diagnosis information based on the recommendation information base corresponding to the user identification and is output to the diagnosis and treatment system, and the medicine information can be accurately recommended.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
FIG. 1 is a schematic flow chart illustrating the establishment of a recommendation information base in an embodiment of the present application;
fig. 2 is a schematic diagram of an information recommendation process in an embodiment of the present application;
fig. 3 is a schematic diagram of an information recommendation process in a second embodiment of the present application;
FIG. 4 is a schematic diagram of an apparatus for implementing the above technique in an embodiment of the present application;
fig. 5 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, method, article, or apparatus.
The technical solution of the present invention will be described in detail with specific examples. Several of the following embodiments may be combined with each other and some details of the same or similar concepts or processes may not be repeated in some embodiments.
The embodiment of the application provides an information recommendation method, which comprises the steps of obtaining diagnosis information provided by a diagnosis and treatment system, generating a user identification of the diagnosis information, obtaining a medicine name corresponding to the diagnosis information based on a recommendation information base corresponding to the user identification, and recommending the medicine name serving as the recommendation information to the diagnosis and treatment system. The scheme can objectively and accurately recommend the medicine information on the premise of meeting the constraint condition.
In the embodiment of the application, a preset synonym library and a recommendation information library need to be established in advance;
in traditional Chinese medicine, aiming at the symptom names and the important names, there are many different aliases and terms, in the embodiment of the present application, all the symptom names representing the same term need to be mapped into a unified symptom expression name, and all the medicine names representing the same term need to be mapped into a unified medicine expression name, and a preset synonym library specifically established includes the following contents:
the corresponding relation between the form name and the symptom unified expression name and the corresponding relation between the medicine name and the medicine unified expression name.
Such as symptom name: insomnia, insomnia and insomnia can all be expressed by using a unified symptom expression name (insomnia), and the preset synonym library comprises the following steps: the correspondence between insomnia and poor sleep, and the correspondence between insomnia and poor sleep may or may not include the correspondence between poor sleep and poor sleep.
If the corresponding relation between the insomnia and the insomnia is included, all symptom names need to be replaced by unified terms in a preset synonym library; otherwise, only the symptom unified expression names corresponding to the symptom names which can be matched in the preset synonym library are used for carrying out unified expression replacement.
Such as the name of the drug: fried rice kernels, raw rice kernels and coix seeds can all be expressed by using a medicine unified expression name (coix seeds), and then the preset synonym library comprises the following steps: the correspondence between parched rice kernels and semen Coicis, and the correspondence between raw rice kernels and semen Coicis may or may not include the correspondence between semen Coicis and semen Coicis.
If the corresponding relation between the coix seeds and the coix seeds is included, all the medicine names need to be subjected to unified term replacement in a preset synonym library; otherwise, only the medicine unified expression names corresponding to the medicine names which can be matched in the preset synonym library are used for carrying out unified expression replacement.
In specific implementation, preset synonym libraries can be respectively established for the drug names and the symptom names, and a preset synonym library can also be established.
In the embodiment of the application, corresponding recommended content is generated for different user identifications, each user identification can correspond to one recommended information base, and recommended information corresponding to a plurality of user identifications respectively can also be stored in one recommended information base.
The user identification here is usually the user identification of an experienced specialist in TCM.
The generation process of the recommendation information base for each user identifier is specifically as follows:
referring to fig. 1, fig. 1 is a schematic flowchart of establishing a recommendation information base in an embodiment of the present application. The method comprises the following specific steps:
The medical record information can be input manually, can be imported into big data, can also be input and acquired by a diagnosis and treatment system, and the mode of acquiring the medical record information is not limited.
The medical record information acquired here includes diagnosis information of a doctor and corresponding medicine information.
In the embodiment of the application, the user identification is not the identification of the patient, but the identification of the doctor.
And 102, acquiring the symptom name and the medicine name in the medical record information.
And segmenting the medical record information, and acquiring the symptom name in the segmented diagnosis information and the medicine name in the medicine information based on a preset medicine table and a symptom table.
When performing word segmentation, an algorithm such as a Conditional Random Field (CRF) can be used for word segmentation.
And 103, uniformly expressing and replacing the symptom names and the medicine names based on the preset synonym library.
If a symptom name or a drug name does not have a corresponding unified expression name in the preset synonym library, the unified expression replacement is not performed.
This may occur because the symptom name or the drug name is already a uniform expression name, and the correspondence is not stored in the preset synonym library, or the symptom name or the drug name is not stored in the preset synonym library.
And 104, establishing a recommendation information base corresponding to the user identification.
Wherein the recommendation information base comprises a corresponding relation between symptom names and medicine names.
The step realizes the establishment of a recommendation information base containing the corresponding relation between the symptom name and the medicine name, and comprises the following steps:
firstly, acquiring the corresponding promotion degree of the symptom name and the medicine name based on a preset association rule extraction algorithm.
The preset association rule extraction algorithm may be Apriori, FP Growth, and the like, but is not limited thereto.
The promotion degree between the symptom name and the medicine name, namely the relevance between the symptom name and the medicine name can be obtained through a preset relevance rule extraction algorithm, and the higher the promotion degree is, the stronger the relevance is, namely, the more frequently the medicine is used for the symptom is.
In specific implementation, the symptom name and the medicine name corresponding to each diagnosis information and each medicine information are used as one piece of information, and the association relation between the symptom name and the medicine name in the information is calculated based on a preset association rule extraction algorithm by using a plurality of pieces of information, namely the association relation is expressed by using the promotion degree.
And secondly, acquiring the symptom name and the medicine name with the promotion degree value larger than 1, establishing a recommendation information base, and recording the promotion degree value corresponding to the symptom name and the medicine name.
For the symptom name and the drug name whose value of the degree of lifting is not more than 1, no correlation exists, and the correspondence relationship is not recorded.
For the same symptom, multiple groups of drug names may be associated, and for convenience of subsequent selection, the corresponding value of the degree of improvement may be recorded for each group of correspondence.
The recommendation information base establishes the corresponding relation between a group of symptom names and a group of medicine names, wherein the group of symptom names can be one symptom name or multiple symptom names, and the group of medicine names can be one medicine name or multiple medicine names.
The following is a description with specific examples:
referring to table 1, table 1 shows content corresponding to medical record information after unified expression name replacement is performed based on a preset synonym library. The corresponding relation between the unified expression names of 5 symptoms and the unified symptom names of the medicines is given in table 1.
Table 1 referring to table 2, table 2 obtains partial correspondence of symptom names and drug names based on the contents of table 1.
| Serial number | Symptom name | Name of drug | Degree of lifting |
| 1 | A white and numb coating | Peach kernel, Loranthus mulberry mistletoe, dodder | 1.11 |
| 2 | Waist soreness | Radix achyranthis bidentatae | 1.67 |
| 3 | Dark tongue | Herba Sedi, caulis et folium Gaultheriae Yunnanensis | 2.5 |
TABLE 2
As shown in table 2, the recommendation information base includes a set of symptom names and a set of drug names, where the set of symptom names may be one symptom name or a plurality of symptom names, and the set of drug names may be one drug name or a plurality of drug names.
In specific implementation, multiple drug combinations corresponding to the same symptom name may also occur, and under the condition that the promotion degree of each corresponding relationship is greater than 1, all the cases need to be recorded, and the cases are selected in the sequence from high promotion degree to low promotion degree in the subsequent selection, or the cases with the drug names with the highest promotion degree except the highest promotion degree are selected as the recommended drug information when the medication rule is violated.
The following describes in detail an implementation of an information recommendation process in an embodiment of the present application with reference to the accompanying drawings.
In practical application scenarios, any user who has permission to use the diagnosis and treatment system or a user who can directly use the information recommendation device provided by the application can use the information recommendation device to perform information recommendation.
Based on the safety of medication, the information recommendation device is deployed in the auxiliary diagnosis and treatment system in the embodiment of the application to recommend the recommendation information for the diagnosis and treatment system, and doctors and the like using the diagnosis and treatment system can manually determine and modify the recommendation information.
Example one
Referring to fig. 2, fig. 2 is a schematic view of an information recommendation process in an embodiment of the present application. The method comprises the following specific steps:
Wherein, the diagnosis information is symptom information given by a doctor aiming at the illness state of the patient;
the user identifier is a user identifier selected in the diagnosis and treatment system, wherein the user identifier is a user identifier of an experienced expert, and the user identifier is input and selected by a person who wants to obtain recommendation information.
The specific implementation of the symptom name in the diagnostic information obtained in this step may be:
the symptom names in the diagnosis information can be obtained by using algorithms such as Conditional Random Field (CRF) and the like to perform word segmentation.
Wherein the recommendation information base comprises a corresponding relation between symptom names and medicine names.
And maintaining a recommendation information base or a part of the recommendation information base aiming at each user identifier, wherein the information recommended in the embodiment of the application needs to be searched in the recommendation information base corresponding to the user identifier.
And step 204, matching the symptom names in the recommendation information base.
In specific implementation, if the symptom names are N, the N symptom names are preferentially used as a group of symptom names for matching; if the matching is not successful, using a plurality of groups of N-1 symptom names for de-matching, if the matching is successful, using the symptom names except N-1 for de-matching, and recommending the medicament names matched twice as a combination; and analogizing in sequence, and outputting non-recommendation information until the N symptom names are matched with the corresponding medicine names or the symptom names which are not matched with the medicine names exist.
If a group of symptom names obtained from the diagnosis information are a symptom name A, a symptom name B and a symptom name C, matching the symptom names A, B and C as a whole in a recommendation information base, and if the symptom names A, B and C are matched, recommending corresponding medicine names; if not, combining the two parts and respectively matching;
and if the symptom name A and the symptom name B are matched, acquiring the medicine names corresponding to the symptom name A and the symptom name B, further acquiring the medicine name corresponding to the symptom name C, and recommending the medicine names corresponding to the symptom name A and the symptom name B and the medicine name corresponding to the symptom name C as recommendation information.
If the symptom names A and B, and the symptom names C and B are matched, a group is randomly selected, or according to a certain rule, if the preset priority is higher than the combination of the symptom names, the medicine name corresponding to the combination of the symptom names is preferentially selected.
If the symptom name A and the symptom name B are not matched, the symptom name A and the symptom name C are not matched, and the symptom name C and the symptom name B are respectively unmatched, and if the symptom name A, the symptom name B and the symptom name C are matched, corresponding medicine names are combined for recommendation; if the unmatched symptom names exist, the corresponding medicine names cannot be determined, and no recommendation information is output.
And step 205, acquiring a medicine name corresponding to the matched symptom name, and outputting the medicine name to the diagnosis and treatment system.
When the diagnosis information and the user identification in the diagnosis and treatment system are obtained, the name of the medicine is recommended for the diagnosis information based on the recommendation information base corresponding to the user identification and is output to the diagnosis and treatment system, and the medicine information can be accurately recommended.
Example two
Referring to fig. 3, fig. 3 is a schematic view of an information recommendation process in the second embodiment of the present application. The method comprises the following specific steps:
Wherein, the diagnosis information is symptom information given by a doctor aiming at the illness state of the patient;
the user identifier is a user identifier selected in the diagnosis and treatment system, wherein the user identifier is a user identifier of an experienced expert, and the user identifier is input and selected by a person who wants to obtain recommendation information.
The specific implementation of the symptom name in the diagnostic information obtained in this step may be:
the symptom names in the diagnosis information can be obtained by using algorithms such as Conditional Random Field (CRF) and the like to perform word segmentation.
And step 303, performing unified expression replacement on the matched symptom names based on a preset synonym library.
During specific implementation, whether each name is uniformly expressed and replaced is related to the implementation of a preset synonym library;
if a symptom name or a medicine name does not have a corresponding unified expression name in the preset synonym library, carrying out unified expression replacement; aiming at different preset synonym libraries, the realization corresponds to different conditions respectively:
when the symptom names which are uniformly expressed names are not stored in the preset synonym library, the corresponding symptom names cannot be matched under the following two conditions:
in the first case:
the symptom name is already a uniform expression name;
in the second case: and when the preset synonym library is established, the establishment of the relevant corresponding relation is not carried out aiming at the symptom names, and the symptom names are newly added.
When symptom names which are unified expression names are stored in a preset synonym library, the following condition exists, and the corresponding symptom names cannot be matched:
and when the preset synonym library is established, the establishment of the relevant corresponding relation is not carried out aiming at the symptom names, and the symptom names are newly added.
And 304, acquiring a recommendation information base corresponding to the user identifier.
Wherein the recommendation information base comprises a corresponding relation between symptom names and medicine names.
And maintaining a recommendation information base or a part of the recommendation information base aiming at each user identifier, wherein the information recommended in the embodiment of the application needs to be searched in the recommendation information base corresponding to the user identifier.
In specific implementation, if the symptom names are N, the N symptom names are preferentially used as a group of symptom names for matching; if the matching is not successful, using a plurality of groups of N-1 symptom names for de-matching, if the matching is successful, using the symptom names except N-1 for de-matching, and recommending the medicament names matched twice as a combination; and analogizing in sequence, and outputting non-recommendation information until the N symptom names are matched with the corresponding medicine names or the symptom names which are not matched with the medicine names exist.
If a group of symptom names obtained from the diagnosis information are a symptom name A, a symptom name B and a symptom name C, matching the symptom names A, B and C as a whole in a recommendation information base, and if the symptom names A, B and C are matched, recommending corresponding medicine names; if not, combining the two parts and respectively matching;
and if the symptom name A and the symptom name B are matched, acquiring the medicine names corresponding to the symptom name A and the symptom name B, further acquiring the medicine name corresponding to the symptom name C, and recommending the medicine names corresponding to the symptom name A and the symptom name B and the medicine name corresponding to the symptom name C as recommendation information.
If the symptom names A and B, and the symptom names C and B are matched, a group is randomly selected, or according to a certain rule, if the preset priority is higher than the combination of the symptom names, the medicine name corresponding to the combination of the symptom names is preferentially selected.
If the symptom name A and the symptom name B are not matched, the symptom name A and the symptom name C are not matched, and the symptom name C and the symptom name B are respectively unmatched, and if the symptom name A, the symptom name B and the symptom name C are matched, corresponding medicine names are combined for recommendation; if the unmatched symptom names exist, the corresponding medicine names cannot be determined, and no recommendation information is output.
And if the matched symptom names correspond to the values of the plurality of degrees of lifting, selecting the medicine name corresponding to the value of the maximum degree of lifting.
In this embodiment of the application, after obtaining the medicine name corresponding to the matched symptom name, before outputting the obtained medicine name to the diagnosis and treatment system, the method further includes:
determining whether incompatibility of the medicines is avoided among all the acquired medicine names, and if so, executing the operation of outputting the acquired medicine names to the diagnosis and treatment system; otherwise, selecting the medicine name corresponding to the value with the next highest maximum promotion degree until whether the incompatibility of the medicines is avoided among all the obtained medicine names or the medicine name cannot be selected, and outputting no recommendation information.
And step 306, acquiring the medicine name corresponding to the matched symptom name, and outputting the medicine name to the diagnosis and treatment system.
When the diagnosis information and the user identification in the diagnosis and treatment system are obtained, the name of the medicine is recommended for the diagnosis information based on the recommendation information base corresponding to the user identification and is output to the diagnosis and treatment system, and the medicine information can be accurately recommended.
EXAMPLE III
After the medicine name is recommended to the diagnosis and treatment system in the embodiment of the application, the medicine name can be modified by the user corresponding to the user identification, if the doctor can determine whether the recommended medicine name is matched with the diagnosis information according to experience, if the medicine name is not reasonable, the medicine name can be modified manually, and the modified medicine name is fed back to the information recommendation device.
If the information recommendation device receives that the name of the medicine input through the diagnosis and treatment system is different from the recommended name of the medicine, the recommendation information base is updated by using the name of the medicine input through the diagnosis and treatment system;
specifically, when updating, for example, the value of the degree of lifting corresponding to the symptom name and the drug name is updated, or the drug name corresponding to the symptom name is updated.
And medical record information can be acquired periodically to update the recommendation information base.
The recommendation information base can be gradually improved, so that the corresponding relation in the recommendation information base is more accurate, and information can be accurately recommended.
Based on the same inventive concept, the embodiment of the application also provides an information recommendation device. Referring to fig. 4, fig. 4 is a schematic structural diagram of an apparatus applied to the above technology in the embodiment of the present application. The device comprises: a establishing unit 401, a first acquiring unit 402, a second acquiring unit 403, a matching unit 404, a third acquiring unit 405 and an output unit 406;
the establishing unit 401 is configured to store a corresponding relationship between the user identifier and the recommendation information base, and the recommendation information base;
a first obtaining unit 402, configured to obtain diagnosis information input by the diagnosis and treatment system, and a user identifier; acquiring a symptom name in the diagnosis information;
a second obtaining unit 403, configured to obtain the recommendation information base corresponding to the user identifier obtained by the first obtaining unit 402 in the establishing unit 401; the recommendation information base comprises a corresponding relation between symptom names and medicine names;
a matching unit 404 for matching the symptom name acquired by the first acquiring unit 402 in the recommendation information base acquired by the second acquiring unit 403;
a third obtaining unit 405, configured to obtain a medicine name corresponding to the symptom name matched by the matching unit 404;
an output unit 406, configured to output the name of the drug acquired by the third acquiring unit 405 to the diagnosis and treatment system.
Preferably, the first and second electrodes are formed of a metal,
the establishing unit 401 is further configured to establish a preset synonym library; the preset synonym library comprises a corresponding relation between symptom names and symptom unified expression names and a corresponding relation between medicine names and medicine unified expression names.
Preferably, the first and second electrodes are formed of a metal,
the first obtaining unit 402 is further configured to perform unified expression replacement on the matched symptom names based on a preset synonym library.
Preferably, the first and second electrodes are formed of a metal,
the establishing unit 401 is further configured to acquire medical record information corresponding to the user identifier; acquiring a symptom name and a medicine name in the medical record information; uniformly expressing and replacing the symptom names and the medicine names based on the preset synonym library; establishing a recommendation information base corresponding to the user identification; wherein the recommendation information base comprises a corresponding relation between symptom names and medicine names.
Preferably, the first and second electrodes are formed of a metal,
the establishing unit 401 is specifically configured to obtain a promotion degree corresponding to the symptom name and the drug name based on a preset association rule extraction model; and acquiring the symptom name and the medicine name with the promotion degree value larger than 1 to establish a recommendation information base, and recording the promotion degree values corresponding to the symptom name and the medicine name.
Preferably, the first and second electrodes are formed of a metal,
a matching unit 404, configured to select a drug name corresponding to the maximum lifting degree value if the matched symptom name corresponds to multiple lifting degree values;
a third obtaining unit 405, configured to determine whether incompatibility of the obtained drug names is avoided, and if so, execute the operation of outputting the obtained drug names to the diagnosis and treatment system; otherwise, selecting the medicine name corresponding to the value with the next highest maximum promotion degree until whether the incompatibility of the medicines is avoided among all the obtained medicine names or the medicine name cannot be selected.
Preferably, the first and second electrodes are formed of a metal,
the establishing unit 401 is further configured to update the recommended information base by using the name of the drug input through the diagnosis and treatment system if the name of the drug input through the diagnosis and treatment system is different from the recommended name of the drug; or, acquiring medical record information periodically to update the recommendation information base.
The units of the above embodiments may be integrated into one body, or may be separately deployed; may be combined into one unit or further divided into a plurality of sub-units.
In another embodiment, an electronic device is also provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the information recommendation method when executing the program.
In another embodiment, a computer-readable storage medium is also provided, having stored thereon computer instructions, which when executed by a processor, perform the steps of the information recommendation method.
Fig. 5 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 5, the electronic device may include: a Processor (Processor)510, a communication Interface (Communications Interface)520, a Memory (Memory)530 and a communication bus 540, wherein the Processor 510, the communication Interface 520 and the Memory 530 communicate with each other via the communication bus 540. Processor 510 may call logic instructions in memory 530 to perform the following method:
acquiring diagnosis information input by a diagnosis and treatment system and a user identifier;
acquiring a symptom name in the diagnosis information;
acquiring a recommendation information base corresponding to the user identification; the recommendation information base comprises a corresponding relation between symptom names and medicine names;
matching the symptom names in the recommendation information base;
and acquiring the medicine name corresponding to the matched symptom name, and outputting the medicine name to the diagnosis and treatment system.
Furthermore, the logic instructions in the memory 530 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. An information recommendation method, characterized in that the method comprises:
acquiring diagnosis information input by a diagnosis and treatment system and a user identifier;
acquiring a symptom name in the diagnosis information;
acquiring a recommendation information base corresponding to the user identification; the recommendation information base comprises a corresponding relation between symptom names and medicine names;
matching the symptom names in the recommendation information base;
and acquiring the medicine name corresponding to the matched symptom name, and outputting the medicine name to the diagnosis and treatment system.
2. The method of claim 1, further comprising:
establishing a preset synonym library; the preset synonym library comprises a corresponding relation between symptom names and symptom unified expression names and a corresponding relation between medicine names and medicine unified expression names.
3. The method of claim 2, wherein after the obtaining of the symptom name in the diagnostic information and before the matching of the symptom name in the recommendation information base, the method further comprises:
and performing unified expression replacement on the matched symptom names based on a preset synonym library.
4. The method of claim 2, further comprising:
acquiring medical record information corresponding to a user identifier;
acquiring a symptom name and a medicine name in the medical record information;
uniformly expressing and replacing the symptom names and the medicine names based on the preset synonym library;
establishing a recommendation information base corresponding to the user identification; wherein the recommendation information base comprises a corresponding relation between symptom names and medicine names.
5. The method according to claim 4, wherein the establishing of the recommendation information base corresponding to the user identifier comprises:
acquiring the corresponding promotion degree of the symptom name and the medicine name based on a preset association rule extraction algorithm;
and acquiring the symptom name and the medicine name with the promotion degree value larger than 1 to establish a recommendation information base, and recording the promotion degree values corresponding to the symptom name and the medicine name.
6. The method according to claim 5, wherein the obtaining of the drug name corresponding to the matched symptom name comprises:
if the matched symptom names correspond to a plurality of values of the promotion degree, selecting the medicine name corresponding to the value of the maximum promotion degree;
after the obtaining of the medicine name corresponding to the matched symptom name and before the outputting of the obtained medicine name to the diagnosis and treatment system, the method further includes:
determining whether incompatibility of the medicines is avoided among all the acquired medicine names, and if so, executing the operation of outputting the acquired medicine names to the diagnosis and treatment system; otherwise, selecting the medicine name corresponding to the value with the next highest maximum promotion degree until whether the incompatibility of the medicines is avoided among all the obtained medicine names or the medicine name cannot be selected.
7. The method according to any one of claims 1-6, wherein the method further comprises:
if the received medicine name input through the diagnosis and treatment system is different from the recommended medicine name, updating the recommended information base by using the medicine name input through the diagnosis and treatment system;
or, acquiring medical record information periodically to update the recommendation information base.
8. An information recommendation apparatus, characterized in that the apparatus comprises: the device comprises an establishing unit, a first acquiring unit, a second acquiring unit, a matching unit, a third acquiring unit and an output unit;
the establishing unit is used for storing the corresponding relation between the user identification and the recommendation information base;
the first acquisition unit is used for acquiring diagnosis information input by the diagnosis and treatment system and a user identifier; acquiring a symptom name in the diagnosis information;
the second obtaining unit is used for obtaining the recommendation information base corresponding to the user identifier obtained by the first obtaining unit in the establishing unit; the recommendation information base comprises a corresponding relation between symptom names and medicine names;
the matching unit is used for matching the symptom name acquired by the first acquiring unit in the recommendation information base acquired by the second acquiring unit;
the third obtaining unit is used for obtaining the medicine name corresponding to the symptom name matched by the matching unit;
the output unit is used for outputting the medicine name acquired by the third acquisition unit to the diagnosis and treatment system.
9. An electronic device 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 method according to any of claims 1-7 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method of any one of claims 1 to 7.
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