CN112289458A - Big data-oriented potential adverse drug reaction data mining system and method - Google Patents
Big data-oriented potential adverse drug reaction data mining system and method Download PDFInfo
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- CN112289458A CN112289458A CN202011351117.6A CN202011351117A CN112289458A CN 112289458 A CN112289458 A CN 112289458A CN 202011351117 A CN202011351117 A CN 202011351117A CN 112289458 A CN112289458 A CN 112289458A
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- 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
Abstract
The invention discloses a big data-oriented potential adverse drug reaction data mining system and a big data-oriented potential adverse drug reaction data mining method, wherein the system comprises the following steps: the system comprises a plurality of acquisition terminals, an electronic medical record acquisition module, a specific medicine data mining module, a cloud terminal adverse event data processing module, a cloud generation module, a main control computer, a human-computer interaction interface and a data exchanger; the method comprises the following steps: step S1, networking the electronic medical record systems of all hospitals; step S2, capturing the patients who take the specific medicine in the electronic medical record system; step S3, establishing a database of relevant drugs and adverse reactions after the drugs are used; step S4, screening data; step S5, classifying the characteristics; the invention analyzes the adverse reaction of the specific medicine by integrating the electronic medical histories of all hospitals, has wider data sources and is available for all kinds of people, thereby being beneficial to analyzing the reaction of the medicine relative to all kinds of people. Useless data can be removed in the analysis process, analysis is facilitated, the workload of data analysis is reduced, and the sample accuracy is improved.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of adverse drug reactions, in particular to the technical field of a potential adverse drug reaction data mining system and method for big data.
[ background of the invention ]
Adverse Drug Reactions (ADRs) are unintended and potentially harmful reactions caused by normal use of drugs. ADR represents a significant public health problem worldwide. Prediction of ADR is very valuable and important for safe drug development and accurate drug therapy. Some drugs can obtain related adverse reactions of a part of drugs after multiple related experiments, but all adverse reactions of the drugs may not be obtained due to the limitation of experimental samples.
In contrast, chinese patent No. CN104765947B proposes a method for mining potential adverse drug reactions data for big data, which includes the following steps: A. capturing a drug adverse event report; B. pre-processing the data of the adverse drug event report of the adverse drug event data set; C. standardizing the medicine name; D. filtering known adverse reactions; E. calculating the degree of association; F. and sorting the relevance. The invention is suitable for the excavation work of potential adverse drug reactions, and is not limited to the types of drugs; can effectively find the potential safety hazard of the marketed drugs. However, the mining method has large calculation amount and complicated samples, cannot perform effective analysis, and cannot perform analysis aiming at a certain specific medicine.
[ summary of the invention ]
The invention aims to solve the problems in the prior art, and provides a big data-oriented potential adverse drug reaction data mining system and method, which can capture medical records so as to analyze specific potential adverse drug reactions.
In order to achieve the purpose, the invention provides a big data-oriented potential adverse drug reaction data mining system, which comprises:
the system comprises a plurality of acquisition terminals, a plurality of monitoring terminals and a plurality of monitoring terminals, wherein the acquisition terminals are arranged in each hospital and are in data communication connection with an electronic medical record system in the hospital;
the electronic medical record acquisition module is connected with the acquisition terminal and used for networking electronic medical record systems of various hospitals and acquiring electronic medical records;
the specific medicine data mining module is connected with the electronic medical record acquisition module and is used for mining the patients who take the specific medicines in the electronic medical records acquired by the electronic medical record acquisition module to acquire related patient information;
the cloud terminal adverse event data processing module is connected with the specific medicine data mining module and used for processing the specific medicine data mining module, capturing adverse event data in the data and establishing a database of related medicines and adverse reactions after the medicines are used;
the cloud generation module is connected with the cloud terminal adverse event data processing module and used for carrying out feature classification on the patient data screened by the cloud terminal adverse event data processing module, classifying similar adverse reactions together and generating a relevant medicine and an adverse reaction database table after the medicine is used;
the main control computer is connected with the cloud generation module and used for receiving and processing the database table generated by the cloud generation module;
the human-computer interaction interface is connected with the main control computer and is used for displaying the data table processed by the main control computer;
and the data switch is used for connecting the specific medicine data mining module with the cloud terminal adverse event data processing module, the cloud generating module and the human-computer interaction interface.
Preferably, the system further comprises a first storage module connected with the specific medicine data mining module for storing data and a second storage module connected with the main control computer for storing data.
Preferably, the system further comprises a data removing module connected with the specific drug data mining module, wherein the data removing module is used for removing the integrity of the information of the relevant patients in the drug user list established by the specific drug data mining module, judging whether the data is missing or not, and removing the information of the relevant drugs and the adverse reaction patients with the missing integrity of the information of the relevant patients in the adverse reaction database after the drugs are used if the data is missing.
Preferably, the system further comprises a known adverse drug reaction database module connected with the cloud terminal adverse event data processing module through the data switch, wherein the database module is used for recording relevant data of known adverse drug reactions.
Another object of the present invention is to provide a method for mining potential adverse drug reaction data facing big data, which comprises the following steps:
step S1, networking the electronic medical record systems of all hospitals;
step S2, capturing patients who take specific medicines in the electronic medical record system, establishing a medicine user list, and acquiring related patient information;
step S3, according to the list of patients captured in step S2, adverse event data in medical records of the patients after using the drugs are extracted, and a database of related drugs and adverse reactions after using the drugs is established;
step S4, screening the relevant drugs and the patient data in the adverse reaction database after the drugs are used, and removing part of useless patient data;
and S5, classifying the characteristics of the patient data screened in the S4, classifying the similar adverse reactions together, generating a relevant medicament and an adverse reaction database table after the medicament is used, and displaying the relevant medicament and the adverse reaction database table through a user interaction interface.
Preferably, the related patient information in step S2 includes sex, age, medical history, and physical constitution information.
Preferably, the step S4 specifically includes the following steps:
step S4-1, according to the electronic medical record of the patient list captured in step S2, capturing whether the patient needs to excavate potential adverse reaction medicines and takes other medicines simultaneously, if so, acquiring a list of medicines taken simultaneously and entering the next step;
s4-2, acquiring a database related to the adverse reaction of the known medicines, and acquiring the adverse reaction characteristics of the list of other medicines taken simultaneously, which are acquired in the S4-1, from the database related to the adverse reaction of the known medicines;
and S4-3, comparing the characteristics of the other adverse drug reactions which are taken simultaneously and acquired in the step S4-2 with the adverse event data in the medical record of the patient after the patient uses the drugs, and removing the patient record with the characteristics close to the characteristics comparison.
Preferably, the step S4 further includes:
and S4-4, introducing a database related to the adverse reaction of the known medicine in a networking manner, acquiring the known adverse reaction characteristics of the specific medicine, performing characteristic comparison on the patient data screened in the S4 and the known adverse reaction characteristics, and removing the list of the known adverse reaction patients of the specific medicine.
Preferably, the step S4 further includes checking the completeness of the related patient information, and determining whether there is a data loss, and if there is a data loss, rejecting the related drug and the adverse reaction patient information with a complete loss of related patient information in the adverse reaction database after the drug is used.
Preferably, the known adverse drug reaction related database is an ADR database of adverse drug reactions in China.
Preferably, the adverse reaction data table in step S5 includes data of the total number of the adverse reaction patients, data of the percentage of the adverse reaction patients in the list of the drug users, and data of the mortality rate of the adverse reaction patients.
Preferably, the adverse reaction data table in step S5 further includes age distribution data of the adverse reaction patients.
Preferably, all the categories of data in the adverse reaction data table of step S5 are sorted in descending order according to the percentage ratio of the adverse reaction patients to the drug user list.
Preferably, the adverse reaction data table in step S5 is used for separate statistics of patients with different sexes.
The big data-oriented potential adverse drug reaction data mining system and method have the beneficial effects that: the invention analyzes the adverse reaction of the specific medicine by integrating the electronic medical histories of all hospitals, has wider data sources, more convenient data acquisition, more complete samples and the functions of all kinds of people, and is beneficial to analyzing the reaction of the medicine relative to all kinds of people. Useless data can be removed in the analysis process, analysis is facilitated, the workload of data analysis is reduced, and the sample accuracy is improved. The adverse reaction database table is established for displaying the analyzed data, so that the potential adverse reaction condition can be conveniently known and analyzed, the calculation process is carried out through the cloud, the system cost is reduced, the system volume is reduced, and the calculation amount is larger.
The features and advantages of the present invention will be described in detail by embodiments in conjunction with the accompanying drawings.
[ description of the drawings ]
FIG. 1 is a schematic structural diagram of a big data-oriented potential ADR data mining system.
In the figure: the system comprises a 1-electronic medical record acquisition module, a 2-specific medicine data mining module, a 3-cloud terminal adverse event data processing module, a 4-cloud generation module, a 5-human-computer interaction interface, a 7-data removing module, a 9-known adverse drug reaction database module, a 10-acquisition terminal, a 11-main control computer, a 12-data switch, a 61-first storage module and a 62-second storage module.
[ detailed description ] embodiments
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood, however, that the description herein of specific embodiments is only intended to illustrate the invention and not to limit the scope of the invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention. In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings or the orientations or positional relationships that the products of the present invention are conventionally placed in when used, and are only used for convenience of describing the present invention and simplifying the description, but do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should also be noted that, unless otherwise explicitly specified or limited, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The first embodiment is as follows:
referring to fig. 1, the invention relates to a big data-oriented potential adverse drug reaction data mining system, which comprises a data mining system.
A plurality of acquisition terminals 10. The acquisition terminal 10 is arranged in each hospital and is connected with the electronic medical record system in the hospital in a data communication way. The system is used for networking electronic medical record systems of various hospitals and collecting electronic medical records.
An electronic medical record acquisition module 1. Connected with the acquisition terminal 10. The system is used for networking electronic medical record systems of various hospitals. And collecting the electronic medical records.
A specific drug data mining module 2. Is connected with the electronic medical record acquisition module 1. The method is used for mining the patients who take the specific medicines in the electronic medical records acquired by the electronic medical record acquisition module 1. Relevant patient information is obtained.
And the cloud terminal adverse event data processing module 3. Connected to the specific drug data mining module 2. For processing the drug-specific data mining module 2 collected data. And capturing adverse event data in the data. Establishing a database of relevant drugs and adverse reactions after the drugs are used. .
A cloud generation module 4. And the cloud terminal adverse event data processing module 3 is connected with the cloud terminal adverse event data processing module. And (4) the method is used for feature classification.
And (4) performing characteristic classification on the patient data screened by the cloud terminal adverse event data processing module 3. Approximate adverse reactions were grouped together. Generating a relevant medicine and adverse reaction database table after using the medicine.
A host computer 11. Connected with the cloud generating module 4. For receiving and processing the database table generated by the cloud generating module 4.
A human-computer interaction interface 5. Connected to a host computer 11. For displaying the data form processed by the host computer 11.
A data switch 12. The system is used for connecting the specific medicine data mining module 2 with the cloud terminal adverse event data processing module 3, the cloud generating module 4 and the human-computer interaction interface 5. The adverse reaction of specific medicines is analyzed by integrating the electronic medical histories of all hospitals, so that the method has the advantages of wider data source, more convenient data acquisition, more complete samples and the like, is available for all kinds of people, and is favorable for analyzing the reaction of medicines to all kinds of people. Potential adverse reactions of the medicine which are not found in clinical tests are excavated. In the embodiment, the calculation process is performed through the cloud terminal adverse event data processing module 3 and the cloud generating module 4, so that the system cost is reduced, the system volume is reduced, and the calculation amount is larger.
Referring to fig. 1, a first storage module 61 connected to the specific drug data mining module 2 for storing data and a second storage module 62 connected to the host computer 11 for storing data are also included. The storage module is used for storing the collected data and the data generated in the analysis process of each step, so that later inquiry and calling are facilitated.
Referring to fig. 1, a data elimination module 7 connected to the specific medicine data mining module 2 is also included. The data eliminating module 7 is used for eliminating the integrity of the information of the relevant patients in the drug user list established by the specific drug data mining module 2 and judging whether the data is missing or not. If the adverse reaction patient information is missing, the adverse reaction patient information with the missing integrity of the related medicine and the related patient information in the adverse reaction database after the medicine is used is removed. Useless data are removed through the data removing module 7, analysis is facilitated, data analysis workload is reduced, and sample accuracy is improved.
Referring to fig. 1, the system further comprises a known adverse drug reaction database module 9 connected with the cloud terminal adverse event data processing module 3 through a data switch 12. The database module 9 is used for recording the data related to the known adverse drug reactions. By introducing the known adverse drug reaction data in the known adverse drug reaction database module 9, the adverse drug reaction data can be conveniently compared with the adverse reaction data recorded in the electronic medical record,
example two:
the invention relates to a big data-oriented potential adverse drug reaction data mining method which comprises the following steps.
Step S1. And (5) networking the electronic medical record systems of all hospitals.
Step S2. And grabbing patients who take specific medicines in the electronic medical record system, establishing a medicine user list, and acquiring related patient information.
Step S3. And (4) according to the list of the patients captured in the step (S2), extracting adverse event data in medical records of the patients after using the medicines, and establishing a database of relevant medicines and adverse reactions after using the medicines.
Step S4. Screening relevant drugs and adverse reaction database patient data after using the drugs, and removing part of useless patient data.
Step S5. And (4) feature classification, namely performing feature classification on the patient data screened in the step S4, classifying the approximate adverse reactions together, generating a relevant medicament and an adverse reaction database table after the medicament is used, and displaying the relevant medicament and the adverse reaction database table through a user interaction interface. The adverse reaction of specific medicines is analyzed by integrating the electronic medical histories of all hospitals, so that the method has the advantages of wider data source, more convenient data acquisition, more complete samples and the like, is available for all kinds of people, and is favorable for analyzing the reaction of medicines to all kinds of people. Useless data can be removed in the analysis process, analysis is facilitated, the workload of data analysis is reduced, and the sample accuracy is improved. And the analyzed data establishes adverse reaction database table display, so that the potential adverse reaction condition can be conveniently known and analyzed.
Further, the relevant patient information in step S2 includes sex, age, medical history, and physical constitution information. The information of the patient is recorded in detail, so that the reaction of the medicine in different people and different constitutions can be conveniently analyzed.
Further, step S4 specifically includes the following steps.
Step S4-1. And (5) according to the electronic medical record of the patient list captured in the step S2, capturing whether the patient needs to take other medicines besides the medicines with the potential adverse reactions. If yes, acquiring a list of medicines taken at the same time and entering the next step.
Step S4-2. And acquiring a database related to the known adverse reaction of the medicine. And acquiring the adverse reaction characteristics of the list of other medicines taken simultaneously, which are acquired in the step S4-1, from a known adverse reaction medicine related database.
And step S4-3. And (4) comparing the characteristics of the other adverse drug reactions which are taken simultaneously and acquired in the step (S4-2) with the adverse event data in the medical record of the patient after the patient uses the drug, and removing the patient record close to the characteristic comparison. Other adverse drug reaction information that the patient took simultaneously is rejected to this embodiment, is convenient for refine the information, is convenient for refine adverse drug reaction information to the medicine that needs the analysis.
Further, step S4 includes step S4-4. And introducing a database related to the known adverse drug reactions in a networked manner. The known adverse reaction characteristics of the specific drug are obtained. And (4) performing characteristic comparison on the patient data screened in the step S4 with known adverse reaction characteristics. A list of known adverse-effect patients who cull a particular drug. The invention aims to analyze the potential adverse reaction of the medicine, eliminate known adverse reaction patients and facilitate the analysis of the potential adverse reaction.
Further, step S4 includes checking the integrity of the relevant patient information. And determines whether data is missing. If the adverse reaction patient information is missing, the adverse reaction patient information with the missing integrity of the related medicine and the related patient information in the adverse reaction database after the medicine is used is removed. By eliminating useless data, analysis is facilitated, data analysis workload is reduced, and sample accuracy is improved.
Furthermore, the known adverse drug reaction related database is an ADR database of adverse drug reactions in China.
Further, the adverse reaction data table in step S5 includes the total number data of the adverse reaction patients, the percentage data of the adverse reaction patients and the drug user list, and the mortality data of the adverse reaction patients. The severity of various potential adverse reactions can be conveniently analyzed.
Further, the adverse reaction data table in step S5 further includes age distribution data of the adverse reaction patients. The incidence of the potential adverse reaction of the medicine in people at all ages is convenient to analyze.
Further, the category data in the adverse reaction data table in step S5 are arranged in descending order according to the percentage ratio of the adverse reaction patients to the drug user list. The data analysis is convenient, and the adverse reactions with the highest morbidity are found, so that corresponding measures are taken.
Further, in step S5, the patients with different sexes are separately counted in the adverse reaction data table. The incidence of the adverse reaction between different sexes is convenient to analyze.
The above embodiments are illustrative of the present invention, and are not intended to limit the present invention, and any simple modifications of the present invention are within the scope of the present invention.
Claims (10)
1. A big-data oriented potential adverse drug reaction data mining system, comprising:
the system comprises a plurality of acquisition terminals (10), a plurality of monitoring terminals and a plurality of monitoring terminals, wherein the acquisition terminals (10) are arranged in each hospital and are in data communication connection with electronic medical record systems in the hospitals;
the electronic medical record acquisition module (1) is connected with the acquisition terminal (10) and is used for networking electronic medical record systems of various hospitals and acquiring electronic medical records;
the specific medicine data mining module (2) is connected with the electronic medical record acquisition module (1) and is used for mining the patients who take specific medicines in the electronic medical records acquired by the electronic medical record acquisition module (1) to acquire related patient information;
the cloud terminal adverse event data processing module (3) is connected with the specific drug data mining module (2) and is used for processing data acquired by the specific drug data mining module (2), capturing adverse event data in the data and establishing a relevant drug and adverse reaction database after the drug is used;
the cloud generation module (4) is connected with the cloud terminal adverse event data processing module (3) and is used for carrying out feature classification on the patient data screened by the cloud terminal adverse event data processing module (3) and classifying similar adverse reactions together to generate a relevant drug and an adverse reaction database table after the drug is used;
the main control computer (11) is connected with the cloud generation module (4) and is used for receiving and processing the database table generated by the cloud generation module (4);
the human-computer interaction interface (5) is connected with the main control computer (11) and is used for displaying the data table processed by the main control computer (11);
the data exchanger (12) is used for connecting the specific medicine data mining module (2) with the cloud terminal adverse event data processing module (3), the cloud generating module (4) and the human-computer interaction interface (5).
2. The big-data oriented potential adverse drug reaction data mining system of claim 1, wherein: the system also comprises a first storage module (61) connected with the specific medicine data mining module (2) for storing data and a second storage module (62) connected with the master control computer (11) for storing data.
3. The big-data oriented potential adverse drug reaction data mining system of claim 1, wherein: the drug administration system is characterized by further comprising a data removing module (7) connected with the specific drug data mining module (2), wherein the data removing module (7) is used for removing the information integrity of the relevant patients in the drug user list established by the specific drug data mining module (2), judging whether data are missing or not, and removing the relevant drugs and adverse reaction patient information with missing information integrity of the relevant patients in an adverse reaction database after the drugs are used if data are missing.
4. A big data-oriented potential adverse drug reaction data mining method is characterized by comprising the following steps:
step S1, networking the electronic medical record systems of all hospitals;
step S2, capturing patients who take specific medicines in the electronic medical record system, establishing a medicine user list, and acquiring related patient information;
step S3, according to the list of patients captured in step S2, adverse event data in medical records of the patients after using the drugs are extracted, and a database of related drugs and adverse reactions after using the drugs is established;
step S4, screening the relevant drugs and the patient data in the adverse reaction database after the drugs are used, and removing part of useless patient data;
and S5, classifying the characteristics of the patient data screened in the S4, classifying the similar adverse reactions together, generating a relevant medicament and an adverse reaction database table after the medicament is used, and displaying the relevant medicament and the adverse reaction database table through a user interaction interface.
5. The method of claim 4, wherein: the relevant patient information in step S2 includes sex, age, medical history, and physical constitution information.
6. The big-data-oriented potential ADR data mining method according to claim 4, wherein the step S4 specifically comprises the following steps:
step S4-1, according to the electronic medical record of the patient list captured in step S2, capturing whether the patient needs to excavate potential adverse reaction medicines and takes other medicines simultaneously, if so, acquiring a list of medicines taken simultaneously and entering the next step;
s4-2, acquiring a database related to the adverse reaction of the known medicines, and acquiring the adverse reaction characteristics of the list of other medicines taken simultaneously, which are acquired in the S4-1, from the database related to the adverse reaction of the known medicines;
and S4-3, comparing the characteristics of the other adverse drug reactions which are taken simultaneously and acquired in the step S4-2 with the adverse event data in the medical record of the patient after the patient uses the drugs, and removing the patient record with the characteristics close to the characteristics comparison.
7. The big-data oriented potential ADR data mining method of claim 6, wherein said step S4 further comprises:
and S4-4, introducing a database related to the adverse reaction of the known medicine in a networking manner, acquiring the known adverse reaction characteristics of the specific medicine, performing characteristic comparison on the patient data screened in the S4 and the known adverse reaction characteristics, and removing the list of the known adverse reaction patients of the specific medicine.
8. The big-data-oriented potential ADR data mining method according to claim 6, wherein the step S4 further comprises checking the completeness of the related patient information, determining whether the data is missing, and if so, rejecting the related drug and the ADR patient information with the missing completeness of the related patient information in the ADR database after the drug is used.
9. The big-data-oriented potential ADR data mining method as claimed in claim 6, wherein the known ADR-related database is the ADR database in China.
10. The big-data-oriented potential adverse drug reaction data mining method of claim 4, wherein: the adverse reaction data table in the step S5 includes the total number data of the adverse reaction patients, the percentage data of the adverse reaction patients and the drug user list, and the mortality data of the adverse reaction patients.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112820418A (en) * | 2021-02-23 | 2021-05-18 | 四川大学华西第二医院 | Big data-based method and system for quickly identifying and disposing adverse drug reactions |
| CN113539502A (en) * | 2021-07-15 | 2021-10-22 | 中国人民解放军联勤保障部队第九六〇医院 | Adverse drug reaction data simulation algorithm based on clinical electronic medical record |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080208914A1 (en) * | 2007-02-27 | 2008-08-28 | Eclinicalworks Llc | Centralized mining of remote medical records databases |
| US20090216555A1 (en) * | 2008-02-22 | 2009-08-27 | Mckesson Automation Inc. | System, method and computer program product for performing automatic surveillance and tracking of adverse events |
| KR20120016553A (en) * | 2010-08-16 | 2012-02-24 | 아주대학교산학협력단 | Method for detecting adverse reaction of intervention using electronic medical record |
| CN110415831A (en) * | 2019-07-18 | 2019-11-05 | 天宜(天津)信息科技有限公司 | A kind of medical treatment big data cloud service analysis platform |
| CN110704526A (en) * | 2019-09-27 | 2020-01-17 | 北京深度制耀科技有限公司 | Method, device and equipment for acquiring adverse event information and storage medium |
| KR20200023689A (en) * | 2018-08-20 | 2020-03-06 | 아주대학교산학협력단 | The method of artificial intelligence(AI)-based adverse drug reactions detection and the system thereof |
| CN111599481A (en) * | 2020-04-22 | 2020-08-28 | 国家药品监督管理局药品评价中心(国家药品不良反应监测中心) | Method and device for monitoring adverse drug reactions and readable medium |
| CN111883219A (en) * | 2020-06-15 | 2020-11-03 | 吴嘉瑞 | Drug adverse reaction data mining method based on decision tree |
| CN111986819A (en) * | 2020-09-01 | 2020-11-24 | 四川大学华西第二医院 | Adverse drug reaction monitoring method and device, electronic equipment and readable storage medium |
-
2020
- 2020-11-26 CN CN202011351117.6A patent/CN112289458A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080208914A1 (en) * | 2007-02-27 | 2008-08-28 | Eclinicalworks Llc | Centralized mining of remote medical records databases |
| US20090216555A1 (en) * | 2008-02-22 | 2009-08-27 | Mckesson Automation Inc. | System, method and computer program product for performing automatic surveillance and tracking of adverse events |
| KR20120016553A (en) * | 2010-08-16 | 2012-02-24 | 아주대학교산학협력단 | Method for detecting adverse reaction of intervention using electronic medical record |
| KR20200023689A (en) * | 2018-08-20 | 2020-03-06 | 아주대학교산학협력단 | The method of artificial intelligence(AI)-based adverse drug reactions detection and the system thereof |
| CN110415831A (en) * | 2019-07-18 | 2019-11-05 | 天宜(天津)信息科技有限公司 | A kind of medical treatment big data cloud service analysis platform |
| CN110704526A (en) * | 2019-09-27 | 2020-01-17 | 北京深度制耀科技有限公司 | Method, device and equipment for acquiring adverse event information and storage medium |
| CN111599481A (en) * | 2020-04-22 | 2020-08-28 | 国家药品监督管理局药品评价中心(国家药品不良反应监测中心) | Method and device for monitoring adverse drug reactions and readable medium |
| CN111883219A (en) * | 2020-06-15 | 2020-11-03 | 吴嘉瑞 | Drug adverse reaction data mining method based on decision tree |
| CN111986819A (en) * | 2020-09-01 | 2020-11-24 | 四川大学华西第二医院 | Adverse drug reaction monitoring method and device, electronic equipment and readable storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 于跃: "基于大数据挖掘的药品不良反应知识整合与利用研究", 医药卫生科技, no. 9, pages 3 - 5 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112820418A (en) * | 2021-02-23 | 2021-05-18 | 四川大学华西第二医院 | Big data-based method and system for quickly identifying and disposing adverse drug reactions |
| CN113539502A (en) * | 2021-07-15 | 2021-10-22 | 中国人民解放军联勤保障部队第九六〇医院 | Adverse drug reaction data simulation algorithm based on clinical electronic medical record |
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