CN113539410B - Hospital pharmacy medicine intelligent classification pushing equipment based on big data - Google Patents
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- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
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- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
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Abstract
The invention discloses intelligent classified pushing equipment for medicines in a hospital pharmacy based on big data, which belongs to the field of classified pushing of medicines and comprises an information acquisition module, a processing module, a transfer module, an information storage cloud, a retrieval module, an alarm module and a pushing module; the information acquisition module is in communication connection with the processing module, the processing module is in communication connection with the transfer module, the alarm module and the pushing module, the transfer module is in communication connection with the information storage cloud, and the information storage cloud is in communication connection with the retrieval module; the information acquisition module is used for acquiring diagnosis information of a patient and transmitting the diagnosis information to the processing module, wherein the diagnosis information comprises focus positions and disease characteristics of the patient; the processing module receives the diagnosis information and the drug information of the patient in the past, constructs a classification model, and inputs the diagnosis information into the classification model. The invention not only can rapidly and accurately push the medicine to the patient, but also can rapidly judge whether the illness state of the patient is recurrent illness state or not, thereby reminding the patient.
Description
Technical Field
The invention relates to a medicine classified pushing device, in particular to a hospital pharmacy medicine intelligent classified pushing device based on big data.
Background
In the medical industry, outpatient treatment is performed on a patient, generally through experience of doctors, and drug information recommendation is given according to current symptoms of the patient; however, the experience of each different physician is different and the symptoms of the patient are different at different times of the same condition. The outpatient doctor can diagnose and confirm the medication according to the current symptoms of the patient and the experience of the doctor, therefore, under the condition of insufficient experience of the doctor, the medication is possibly not standard, even though the experience of the doctor is sufficient, the symptoms of different periods of the patient disease development can be caused, if the information record of the early period of the disease development is incomplete, the medication information can be inaccurate, in order to ensure the accuracy of the medication which is pushed to the patient by the pharmacy, a large database is generally established in the pharmacy, and the corresponding medication is quickly pushed according to the diagnosis information of the patient by the doctor
The existing medicine classification pushing equipment classifies medicines in a large database of a pharmacy, and after a pharmacy staff transmits diagnosis information to the large database, the medicines are rapidly screened out from the classified large database.
The existing patients take the medicine after taking the medicine, however, some patients break the medicine taking after the sensory state is improved, and a complete treatment course is not formed, so that the disease is easy to relapse.
The existing medicine classification pushing equipment has the following problems: the accurate and effective medicine can not be quickly pushed to the patient, and whether the illness state of the patient is recurrent or not can not be quickly judged. Therefore, a person skilled in the art provides a hospital pharmacy medicine intelligent classification pushing device based on big data so as to solve the problems in the background art.
Disclosure of Invention
The invention aims to provide intelligent classified pushing equipment for medicines in a hospital pharmacy based on big data, which not only can rapidly and accurately push medicines to patients, but also can rapidly judge whether the illness state of the patients is recurrent or not, and further reminds the patients, so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the intelligent classified pushing equipment for the hospital pharmacy medicines based on big data comprises an information acquisition module, a processing module, a transfer module, an information storage cloud, a retrieval module, an alarm module and a pushing module;
the information acquisition module is in communication connection with the processing module, the processing module is in communication connection with the transfer module, the alarm module and the pushing module, the transfer module is in communication connection with the information storage cloud, and the information storage cloud is in communication connection with the retrieval module;
the information acquisition module is used for acquiring diagnosis information of a patient and transmitting the diagnosis information to the processing module, wherein the diagnosis information comprises focus positions and disease characteristics of the patient; the processing module receives diagnosis information and medicine information of a patient in the past, builds a classification model, inputs the diagnosis information into the classification model to obtain a classification result, sends the medicine information to the pushing module according to the classification result, sends the medicine information to the transfer module, builds a disease recurrence detection model, inputs the medicine information of the patient in the past and the medicine information of the patient in the past into the disease recurrence detection model, and sends the detection result to the alarm module; the alarm module receives the detection result and decides whether to send out an alarm according to the detection result; the pushing module receives the drug information and pushes the drug information to a patient; the transfer module receives the medicine information and sends the medicine information to the information storage cloud for storage, the information storage cloud sends the stored medicine information of the patient in the past to the processing module, and the retrieval module enables pharmacy staff to call the medicine taking information of the patient in a corresponding time period at any time;
the specific construction process of the classification model comprises the following steps:
s1: the diagnosis information is classified for the first time according to the focus position, specifically:
s101: extracting focus positions in the diagnosis information, wherein the focus positions are marked as Pi, and i= … … n;
s102: classifying the drugs in the pharmacy for Pi lesion locations, labeled Qj, j= … … n;
s103: outputting the classified medicines;
s2: the diagnosis information is classified for the second time according to the disease condition characteristics, specifically:
s201: extracting disease characteristics in the diagnosis information, wherein the disease characteristics are marked as Ki, i= … … n;
s202: the disease feature corresponding to Qj is marked as Qjz, and z= … … n;
s203: matching Ki with Qjz, and outputting a result that no corresponding medicine exists in a pharmacy if any Ki is not matched with any Qjz; if matching, go to the next step, where i= … … n, j= … … n, z= … … n;
s204: if Ki is completely matched with any one Qjz, outputting the drug information corresponding to Qjz; if Ki is not completely matched with any one Qjz, a drug information priority list is established according to the matched coincidence degree, and the output result is the drug information priority list, wherein i= … … n, j= … … n and z= … … n;
the specific construction process of the disease recurrence detection model comprises the following steps:
step one: the patient current drug information is labeled Si, i= … … n;
step two: patient this time drug information was labeled Di, i= … … n;
step three: si is matched with Di, if any Si is not matched with any Di, a detection result is output, and the illness state is free from recurrence; if matched, entering the next step, wherein i= … … n;
step four: marking the medicine taking time of Si as t1;
step five: marking the medicine taking time of Di as t2;
step six: calculating an interval period T=t2-T1;
step seven: if T exceeds the preset range, outputting a detection result that the illness state recurs but is in a safe period; if T is within the preset range, outputting the detection result that the illness state is recurrent and is not in the safety period.
The invention not only can rapidly and accurately push the medicine to the patient, but also can rapidly judge whether the illness state of the patient is recurrent illness state or not, thereby reminding the patient.
As a further scheme of the invention: the alarm module is a light alarm, and the specific alarm process is as follows:
(1): if the detection result output by the disease recurrence detection model is that the disease does not recur, the light alarm turns on a green light;
(2): if the detection result output by the disease recurrence detection model is that the disease is recurrent but within the safety period, the light alarm lights up a yellow lamp;
(3): if the detection result output by the disease recurrence detection model is that the disease is recurrent and is not in a safety period, the light alarm lights up a red light.
The light alarm can remind the patient to attach importance to the recurrence of the illness state.
As still further aspects of the invention: the specific establishment process of the drug information priority list comprises the following steps:
1): if the matching coincidence degree of Ki and any one Qjz is within 1-45%, marking the drug information corresponding to Qjz as a first-level priority;
2): if the matching coincidence degree of Ki and any one Qjz is within 46-90%, marking the drug information corresponding to Qjz as a second-level priority;
3): if the matching coincidence degree of Ki and any one Qjz is within 91-99%, marking the drug information corresponding to Qjz as three-level priority;
4): and establishing a priority list, wherein the drug information corresponding to the three-level priority Qjz is listed in the front edge of the list and sequentially ordered according to the priority.
The medicine information priority list is convenient for the patient to check the corresponding medicine, and then the medicine which is most suitable for the patient is screened out.
As still further aspects of the invention: the information storage cloud also counts different drug information of different patients to establish a statistical analysis chart, and pharmacy staff can view the statistical analysis chart at any time through the retrieval module.
The statistical analysis chart can more clearly represent different drug information of different patients.
As still further aspects of the invention: the specific construction process of the statistical analysis chart comprises the following steps:
a: dividing one year into four time periods according to spring, summer, autumn and winter;
b: counting the first ten medicines in the quantity of each time period;
c: and C, establishing a statistical analysis chart according to the statistical result of the step B.
The statistical analysis chart can be used for rapidly knowing diseases with higher morbidity in different seasons and corresponding medicines.
As still further aspects of the invention: the statistical analysis graph comprises a bar graph and a sector graph.
The bar graph can be more directly white than the fan graph, indicating the comparison of the top ten medications in quantity.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the diagnosis information is classified for the first time through the focus position, and then the diagnosis information is classified for the second time through the disease condition characteristics, and when the diagnosis information is classified for the second time, a corresponding medicine information priority list can be established according to the matched coincidence ratio, so that medicines can be quickly and accurately pushed to patients, and the patients can conveniently check the corresponding medicines to screen out the medicines most suitable for the patients. In addition, by constructing a disease recurrence detection model, whether the disease of the patient is a recurrent disease or not is rapidly detected, and then the patient is reminded. The establishment of the statistical analysis chart can quickly know diseases with higher morbidity and corresponding medicines in different seasons, so that a hospital is convenient to remind a patient to prevent the easily-transmitted diseases, and a pharmacy can be convenient to timely supplement the medicines corresponding to the easily-transmitted diseases, and the phenomenon of medicine shortage is avoided.
Drawings
Fig. 1 is a block diagram of the structure of the present invention.
Detailed Description
Referring to fig. 1, in an embodiment of the invention, an intelligent classified pushing device for a hospital pharmacy drug based on big data comprises an information acquisition module, a processing module, a transfer module, an information storage cloud, a retrieval module, an alarm module and a pushing module; the information acquisition module is in communication connection with the processing module, the processing module is in communication connection with the transfer module, the alarm module and the pushing module, the transfer module is in communication connection with the information storage cloud, and the information storage cloud is in communication connection with the retrieval module; the information acquisition module is used for acquiring diagnosis information of a patient and transmitting the diagnosis information to the processing module, wherein the diagnosis information comprises focus positions and disease characteristics of the patient; the processing module receives diagnosis information and medicine information of a patient in the past, builds a classification model, inputs the diagnosis information into the classification model to obtain a classification result, sends the medicine information to the pushing module according to the classification result, sends the medicine information to the transfer module, builds a disease recurrence detection model, inputs the medicine information of the patient in the past and the medicine information of the patient in the past into the disease recurrence detection model, and sends the detection result to the alarm module; the alarm module receives the detection result and decides whether to send out an alarm according to the detection result; the pushing module receives the drug information and pushes the drug information to the patient; the transfer module receives the medicine information and sends the medicine information to the information storage cloud for storage, the information storage cloud sends the stored medicine information of the patient in the past to the processing module, and the retrieval module enables a pharmacy staff to take the medicine taking information of the patient in a corresponding time period at any time;
the specific construction process of the classification model comprises the following steps:
s1: the diagnosis information is classified for the first time according to the focus position, specifically:
s101: extracting focus positions in the diagnosis information, wherein the focus positions are marked as Pi, and i= … … n;
s102: classifying the drugs in the pharmacy for Pi lesion locations, labeled Qj, j= … … n;
s103: outputting the classified medicines;
s2: the diagnosis information is classified for the second time according to the disease condition characteristics, specifically:
s201: extracting disease characteristics in the diagnosis information, wherein the disease characteristics are marked as Ki, i= … … n;
s202: the disease feature corresponding to Qj is marked as Qjz, and z= … … n;
s203: matching Ki with Qjz, and outputting a result that no corresponding medicine exists in a pharmacy if any Ki is not matched with any Qjz; if matching, go to the next step, where i= … … n, j= … … n, z= … … n;
s204: if Ki is completely matched with any one Qjz, outputting the drug information corresponding to Qjz; if Ki is not completely matched with any one Qjz, a drug information priority list is established according to the matched coincidence degree, and the output result is the drug information priority list, wherein i= … … n, j= … … n and z= … … n;
the specific construction process of the disease recurrence detection model is as follows:
step one: the patient current drug information is labeled Si, i= … … n;
step two: patient this time drug information was labeled Di, i= … … n;
step three: si is matched with Di, if any Si is not matched with any Di, a detection result is output, and the illness state is free from recurrence; if matched, entering the next step, wherein i= … … n;
step four: marking the medicine taking time of Si as t1;
step five: marking the medicine taking time of Di as t2;
step six: calculating an interval period T=t2-T1;
step seven: if T exceeds the preset range, outputting a detection result that the illness state recurs but is in a safe period; if T is within the preset range, outputting the detection result that the illness state is recurrent and is not in the safety period.
The invention not only can rapidly and accurately push the medicine to the patient, but also can rapidly judge whether the illness state of the patient is recurrent illness state or not, thereby reminding the patient.
In this embodiment: the alarm module is a light alarm, and the specific alarm process is as follows:
(1): if the detection result output by the disease recurrence detection model is that the disease does not recur, the light alarm turns on a green light;
(2): if the detection result output by the disease recurrence detection model is that the disease is recurrent but within the safety period, the light alarm lights up a yellow lamp;
(3): if the detection result output by the disease recurrence detection model is that the disease is recurrent and is not in a safety period, the light alarm lights up a red light.
The light alarm can remind the patient to attach importance to the recurrence of the illness state.
In this embodiment: the specific establishment process of the drug information priority list comprises the following steps:
1): if the matching coincidence degree of Ki and any one Qjz is within 1-45%, marking the drug information corresponding to Qjz as a first-level priority;
2): if the matching coincidence degree of Ki and any one Qjz is within 46-90%, marking the drug information corresponding to Qjz as a second-level priority;
3): if the matching coincidence degree of Ki and any one Qjz is within 91-99%, marking the drug information corresponding to Qjz as three-level priority;
4): and establishing a priority list, wherein the drug information corresponding to the three-level priority Qjz is listed in the front edge of the list and sequentially ordered according to the priority.
The medicine information priority list is convenient for the patient to check the corresponding medicine, and then the medicine which is most suitable for the patient is screened out.
In this embodiment: the information storage cloud also counts different drug information of different patients to establish a statistical analysis chart, and pharmacy staff can check the statistical analysis chart at any time through the retrieval module. The statistical analysis chart can more clearly represent different drug information of different patients.
In this embodiment: the specific construction process of the statistical analysis chart comprises the following steps:
a: dividing one year into four time periods according to spring, summer, autumn and winter;
b: counting the first ten medicines in the quantity of each time period;
c: and C, establishing a statistical analysis chart according to the statistical result of the step B.
The statistical analysis chart can be used for rapidly knowing diseases with higher morbidity in different seasons and corresponding medicines.
In this embodiment: the statistical analysis graph comprises a bar graph and a sector graph. The bar graph can be more directly white than the fan graph, indicating the comparison of the top ten medications in quantity.
The working principle of the invention is as follows: when the diagnosis information processing system is used, firstly, the information acquisition module acquires diagnosis information of a patient and transmits the diagnosis information to the processing module, the processing module receives the diagnosis information and the drug information of the patient in the past period, then, the diagnosis information is input into the classification model to obtain a classification result, the drug information is sent to the pushing module according to the classification result, and meanwhile, the drug information of the patient in the past period and the drug information of the patient in the past period are input into the illness state recurrence detection model, and the detection result is sent to the alarm module. The alarm module receives the detection result and decides whether to send out an alarm according to the detection result, the pushing module receives the drug information and pushes the drug information to the patient, the transfer module receives the drug information and sends the drug information to the information storage cloud for storage, the retrieval module enables pharmacy staff to take the drug information of the patient in a corresponding time period at any time, the information storage cloud also counts different drug information of different patients to establish a statistical analysis chart, and the pharmacy staff can check the statistical analysis chart at any time through the retrieval module.
According to the invention, the diagnosis information is classified for the first time through the focus position, and then the diagnosis information is classified for the second time through the disease condition characteristics, and when the diagnosis information is classified for the second time, a corresponding medicine information priority list can be established according to the matched coincidence ratio, so that medicines can be quickly and accurately pushed to patients, and the patients can conveniently check the corresponding medicines to screen out the medicines most suitable for the patients. In addition, by constructing a disease recurrence detection model, whether the disease of the patient is a recurrent disease or not is rapidly detected, and then the patient is reminded. The establishment of the statistical analysis chart can quickly know diseases with higher morbidity and corresponding medicines in different seasons, so that a hospital is convenient to remind a patient to prevent the easily-transmitted diseases, and a pharmacy can be convenient to timely supplement the medicines corresponding to the easily-transmitted diseases, and the phenomenon of medicine shortage is avoided.
The foregoing description is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical solution of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (6)
1. The intelligent classified pushing equipment for the hospital pharmacy medicines based on the big data is characterized by comprising an information acquisition module, a processing module, a transfer module, an information storage cloud, a retrieval module, an alarm module and a pushing module;
the information acquisition module is in communication connection with the processing module, the processing module is in communication connection with the transfer module, the alarm module and the pushing module, the transfer module is in communication connection with the information storage cloud, and the information storage cloud is in communication connection with the retrieval module;
the information acquisition module is used for acquiring diagnosis information of a patient and transmitting the diagnosis information to the processing module, wherein the diagnosis information comprises focus positions and disease characteristics of the patient; the processing module receives diagnosis information and medicine information of a patient in the past, builds a classification model, inputs the diagnosis information into the classification model to obtain a classification result, sends the medicine information to the pushing module according to the classification result, sends the medicine information to the transfer module, builds a disease recurrence detection model, inputs the medicine information of the patient in the past and the medicine information of the patient in the past into the disease recurrence detection model, and sends the detection result to the alarm module; the alarm module receives the detection result and decides whether to send out an alarm according to the detection result; the pushing module receives the drug information and pushes the drug information to a patient; the transfer module receives the medicine information and sends the medicine information to the information storage cloud for storage, the information storage cloud sends the stored medicine information of the patient in the past to the processing module, and the retrieval module enables pharmacy staff to call the medicine taking information of the patient in a corresponding time period at any time;
the specific construction process of the classification model comprises the following steps:
s1: the diagnosis information is classified for the first time according to the focus position, specifically:
s101: extracting focus positions in the diagnosis information, wherein the focus positions are marked as Pi, and i= … … n;
s102: classifying the drugs in the pharmacy for Pi lesion locations, labeled Qj, j= … … n;
s103: outputting the classified medicines;
s2: the diagnosis information is classified for the second time according to the disease condition characteristics, specifically:
s201: extracting disease characteristics in the diagnosis information, wherein the disease characteristics are marked as Ki, i= … … n;
s202: the disease feature corresponding to Qj is marked as Qjz, and z= … … n;
s203: matching Ki with Qjz, and outputting a result that no corresponding medicine exists in a pharmacy if any Ki is not matched with any Qjz; if matching, go to the next step, where i= … … n, j= … … n, z= … … n;
s204: if Ki is completely matched with any one Qjz, outputting the drug information corresponding to Qjz; if Ki is not completely matched with any one Qjz, a drug information priority list is established according to the matched coincidence degree, and the output result is the drug information priority list, wherein i= … … n, j= … … n and z= … … n;
the specific construction process of the disease recurrence detection model comprises the following steps:
step one: the patient current drug information is labeled Si, i= … … n;
step two: patient this time drug information was labeled Di, i= … … n;
step three: si is matched with Di, if any Si is not matched with any Di, a detection result is output, and the illness state is free from recurrence; if matched, entering the next step, wherein i= … … n;
step four: marking the medicine taking time of Si as t1;
step five: marking the medicine taking time of Di as t2;
step six: calculating an interval period T=t2-T1;
step seven: if T exceeds the preset range, outputting a detection result that the illness state recurs but is in a safe period; if T is within the preset range, outputting the detection result that the illness state is recurrent and is not in the safety period.
2. The intelligent classified pushing equipment for the medicines in the hospital pharmacy based on big data according to claim 1, wherein the alarm module is a light alarm, and the specific alarm process is as follows:
(1): if the detection result output by the disease recurrence detection model is that the disease does not recur, the light alarm turns on a green light;
(2): if the detection result output by the disease recurrence detection model is that the disease is recurrent but within the safety period, the light alarm lights up a yellow lamp;
(3): if the detection result output by the disease recurrence detection model is that the disease is recurrent and is not in a safety period, the light alarm lights up a red light.
3. The intelligent classified pushing device for hospital pharmacy medicines based on big data according to claim 1, wherein the specific establishment process of the medicine information priority list is as follows:
1): if the matching coincidence degree of Ki and any one Qjz is within 1-45%, marking the drug information corresponding to Qjz as a first-level priority;
2): if the matching coincidence degree of Ki and any one Qjz is within 46-90%, marking the drug information corresponding to Qjz as a second-level priority;
3): if the matching coincidence degree of Ki and any one Qjz is within 91-99%, marking the drug information corresponding to Qjz as three-level priority;
4): and establishing a priority list, wherein the drug information corresponding to the three-level priority Qjz is listed in the front edge of the list and sequentially ordered according to the priority.
4. The intelligent classified pushing device for the medicines in the hospital pharmacy based on big data according to claim 1, wherein the information storage cloud end is used for further counting different medicine information of different patients to establish a statistical analysis chart, and pharmacy staff can view the statistical analysis chart at any time through a retrieval module.
5. The intelligent classified pushing device for the hospital pharmacy medicine based on big data according to claim 4, wherein the specific construction process of the statistical analysis chart is as follows:
a: dividing one year into four time periods according to spring, summer, autumn and winter;
b: counting the first ten medicines in the quantity of each time period;
c: and C, establishing a statistical analysis chart according to the statistical result of the step B.
6. The intelligent classified pushing device for hospital pharmacy medicines based on big data according to claim 5, wherein the statistical analysis chart comprises a column chart and a sector chart.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106845118A (en) * | 2017-01-25 | 2017-06-13 | 济南德健信息技术有限公司 | A kind of auxiliary based on electronic health record shared document examines medication system and method |
| CN111161891A (en) * | 2019-12-31 | 2020-05-15 | 重庆亚德科技股份有限公司 | Traditional Chinese medicine information management platform |
| CN111937079A (en) * | 2018-01-25 | 2020-11-13 | 珀尔·霍尔 | Compositions and methods for monitoring treatment of breast conditions |
| CN112116978A (en) * | 2020-09-17 | 2020-12-22 | 陕西师范大学 | Method, system and device for recommending rheumatism immunity medicine |
| CN112786177A (en) * | 2021-03-19 | 2021-05-11 | 南京市中医院 | PRII management platform for UC doctors and patients |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8301468B2 (en) * | 2000-05-15 | 2012-10-30 | Optuminsight, Inc. | System and method of drug disease matching |
-
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106845118A (en) * | 2017-01-25 | 2017-06-13 | 济南德健信息技术有限公司 | A kind of auxiliary based on electronic health record shared document examines medication system and method |
| CN111937079A (en) * | 2018-01-25 | 2020-11-13 | 珀尔·霍尔 | Compositions and methods for monitoring treatment of breast conditions |
| CN111161891A (en) * | 2019-12-31 | 2020-05-15 | 重庆亚德科技股份有限公司 | Traditional Chinese medicine information management platform |
| CN112116978A (en) * | 2020-09-17 | 2020-12-22 | 陕西师范大学 | Method, system and device for recommending rheumatism immunity medicine |
| CN112786177A (en) * | 2021-03-19 | 2021-05-11 | 南京市中医院 | PRII management platform for UC doctors and patients |
Non-Patent Citations (1)
| Title |
|---|
| Clustering-based approach for detecting breast cancer recurrence;Smaranda Belciug等;《2010 10th International Conference on Intelligent Systems Design and Applications》;533-538 * |
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