CN115512856A - Intelligent medical system capable of preventing infectious diseases based on urban big data - Google Patents
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Abstract
The invention discloses an intelligent medical system capable of preventing infectious diseases based on urban big data, which relates to the technical field of data processing and comprises an infectious disease sample library module, an infectious disease analysis system and a public health response system, wherein the infectious disease sample library module collects infectious disease samples based on the big data and establishes an infectious disease sample library; the infectious disease analysis system is used for collecting suspected infectious disease reports, classifying and analyzing the collected suspected infectious disease reports, the public health response system is used for carrying out emergency response on commands transmitted from the infectious disease analysis system, and the public health response system comprises a public emergency response platform and an emergency plan module. The invention can screen when the infectious disease occurs, positively responds to suspected infectious disease, can effectively prevent the infectious disease from spreading in the early stage, and avoids the large-scale spreading of the infectious disease.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an intelligent medical system capable of preventing infectious diseases based on urban big data.
Background
The infectious disease is characterized by pathogen, infectivity and epidemic, and after infection, the infectious disease is always immune, and the prevention of the infectious disease should adopt comprehensive measures taking cutting off main transmission links as a leading part. The transmission and prevalence of infectious diseases must have 3 links, namely, the infection source (human or animal capable of expelling pathogens), the transmission path (path for the pathogens to infect other people) and the susceptible population (people without immunity to the infectious diseases), and if one link can be completely cut off, the occurrence and prevalence of the infectious diseases can be prevented.
For example, the application number CN202010836935.9 is a system, a method and applications thereof for realizing rapid tracing, screening and management of key crowds based on big data and artificial intelligence technology in infectious disease prevention and control, realizing multi-department joint work linkage, enabling leadership decision command and basic level execution, improving management efficiency by intelligent and automatic means, and improving comprehensive management capability and treatment response capability of public health emergencies in operation of ultra-large cities.
The access control system similar to the above application has the following disadvantages:
the infection diseases can not be dealt with timely and effectively when going out, which causes that the large-scale outbreak of the infection diseases at the initial stage can not be prevented timely and actively.
In view of the above, research and improvement are made for the existing structure and deficiency, and an intelligent medical system capable of preventing infectious diseases based on urban big data is provided.
Disclosure of Invention
The present invention is directed to provide an intelligent medical system for preventing infectious diseases based on urban big data, so as to solve the problems of the background art.
In order to achieve the purpose, the invention provides the following technical scheme: an intelligent medical system capable of preventing infectious diseases based on urban big data comprises an infectious disease sample library module, an infectious disease analysis system and a public health response system,
the infectious disease sample library module collects infectious disease samples based on big data and establishes an infectious disease sample library, and the infectious disease sample library module is electrically connected with the infectious disease analysis system;
the infectious disease analysis system is used for collecting suspected infectious disease reports and classifying and analyzing the collected suspected infectious disease reports, and comprises an infrastructure module and a report classification module, wherein the infrastructure module is used for providing the suspected infectious disease reports based on hospital resources in a system coverage area and detection laboratory resources in the system coverage area, the infrastructure module is electrically connected with the report classification module, and the report classification module is used for classifying the suspected infectious disease reports in the infrastructure module, analyzing the classified suspected infectious disease reports and outputting an analysis result to a public health response system;
the public health response system is used for carrying out emergency response on commands transmitted from the infectious disease analysis system and comprises a public emergency response platform and an emergency plan module, the public emergency response platform is used for conducting unified command and dispatching after infectious diseases appear, and the emergency plan module is used for storing emergency plans formulated for all infectious disease outbreaks.
Furthermore, the infectious disease sample library module collects infectious disease samples issued by a public department through big data and models the infectious disease samples, the infectious disease sample library module comprises a sample input unit, a sample modeling unit and a sample library storage, the sample input unit is electrically connected with the sample modeling unit, the sample input unit collects the infectious disease samples and inputs the infectious disease samples into the sample modeling unit, the output end of the sample modeling unit is electrically connected with the sample library storage, the sample modeling unit is used for establishing infectious sample models for the infectious samples and storing the infectious sample models in the sample library storage, the sample library storage is used for storing the infectious sample models, and the sample library storage is connected with a cloud server.
Further, the infrastructure module includes a hospital data collection unit, a detection laboratory data collection unit, and an emergency report data port, the hospital data collection unit is configured to enter a suspected infectious disease report reported in a hospital in a system coverage area, the detection laboratory data collection unit is configured to enter a suspected infectious disease report reported in a detection laboratory in the system coverage area, and the emergency report data port is configured to enter a suspected infectious disease report reported through a personal port.
Further, the report classification module comprises a suspected infectious disease report modeling unit, a primary comparison classification unit and a secondary comparison classification unit, the suspected infectious disease report modeling unit is used for modeling the suspected infectious disease report from the infrastructure module to form a suspected infectious disease model, and the output end of the suspected infectious disease report modeling unit is electrically connected with the primary comparison classification unit.
Further, the primary comparison and classification unit is configured to compare the suspected infectious disease model established in the suspected infectious disease report modeling unit with the infectious sample model stored in the sample library storage, transmit a suspected infectious disease model forming command a to the public health response system if the similarity between the suspected infectious disease model and the infectious sample model is higher than eighty-five percent, and change the suspected infectious disease model into a secondary suspected model and transmit the secondary suspected model to the secondary comparison and classification unit if the similarity between the suspected infectious disease model and the infectious sample model is lower than eighty-five percent.
Further, the secondary comparison classification unit receives secondary suspected models from the primary comparison classification unit, the secondary comparison classification unit compares the received secondary suspected models within forty-eight hours, and if the similarity between the secondary suspected models is higher than eighty-five percent, a secondary suspected model forming command b is transmitted to the public health response system.
Furthermore, the public emergency response platform comprises a regulation and control center unit, a hospital response unit and a local unit response unit, wherein the output end of the regulation and control center unit is electrically connected with the hospital response unit and the local unit response unit respectively, the regulation and control center unit receives a command a and a command b from the infrastructure module and starts different emergency plans stored in the emergency plan module according to different commands, the hospital response unit is applied to the hospital in the system coverage to respond to different emergency plans, and the local unit response unit is applied to the local units in the system coverage to respond to different emergency plans.
Further, the local units in the local unit response unit include cities, districts, counties, towns, streets, villages and natural villages within the coverage area of the system.
The invention provides an intelligent medical system capable of preventing infectious diseases based on urban big data, which has the following beneficial effects: the method can be used for screening when the infectious diseases occur, carrying out positive response on suspected infectious diseases, effectively preventing the infectious diseases at the initial stage of transmission and avoiding the large-scale spread of the infectious diseases.
1. The method comprises the steps that suspected infectious disease reports are collected through an infectious disease analysis system, and the collected suspected infectious disease reports are classified and analyzed, specifically, an infrastructure module provides the suspected infectious disease reports based on hospital resources in a system coverage area and detection laboratory resources in the system coverage area, a report classification module classifies the suspected infectious disease reports in the infrastructure module, analyzes the classified suspected infectious disease reports and outputs an analysis result to a public health response system, specifically, the suspected infectious disease reports reported in a hospital in the system coverage area are recorded through a hospital data collection unit, the suspected infectious disease reports reported in the detection laboratory in the system coverage area are recorded through a detection laboratory data collection unit, and an emergency report data port reports the suspected infectious disease reports reported through a personal port.
2. The suspected infectious disease report modeling unit is used for modeling the suspected infectious disease report from the infrastructure module to form a suspected infectious disease model, the primary comparison and classification unit is used for comparing the suspected infectious disease model established in the suspected infectious disease report modeling unit with an infectious sample model stored in a sample bank storage, if the similarity between the suspected infectious disease model and the infectious sample model is higher than eighty-five percent, a suspected infectious disease model forming command a is transmitted to a public health response system, if the similarity between the suspected infectious disease model and the infectious sample model is lower than eighty-five percent, the suspected infectious disease model is changed into a secondary suspected model which is transmitted to the secondary comparison and classification unit, the secondary comparison and classification unit receives the secondary suspected model from the primary comparison and classification unit, the secondary suspected model is compared with each other within forty-eight hours, and if the similarity between the suspected models is higher than eighty-five percent, the suspected disease model forming command b is transmitted to the public health response system.
Drawings
FIG. 1 is a schematic flow chart of an intelligent medical system for preventing infectious diseases based on urban big data according to the present invention;
FIG. 2 is a schematic flow chart of an infectious disease sample library module of the intelligent medical system for preventing infectious diseases based on urban big data according to the present invention;
FIG. 3 is a schematic flow chart of an infectious disease analysis system of an intelligent medical system for preventing infectious diseases based on urban big data according to the present invention;
FIG. 4 is a schematic flow chart of a public health response system of the intelligent medical system for preventing infectious diseases based on urban big data according to the present invention.
In the figure: 1. an infectious disease sample library module; 101. a sample entry unit; 102. a sample modeling unit; 103. a sample bank memory; 2. an infectious disease analysis system; 201. an infrastructure module; 201a, a hospital data collection unit; 201b, a detection laboratory data collection unit; 201c, an emergency report data port; 202. a report classification module; 202a, a suspected infectious disease report modeling unit; 202b, a primary comparison and classification unit; 202c, a secondary comparison classification unit; 3. a public health response system; 301. a public emergency response platform; 301a, a regulatory central unit; 301b, a hospital response unit; 301c, local unit response unit; 302. and an emergency plan module.
Detailed Description
Referring to fig. 1-4, the present invention provides a technical solution: an intelligent medical system capable of preventing infectious diseases based on urban big data comprises an infectious disease sample library module 1, an infectious disease analysis system 2 and a public health response system 3,
the infectious disease sample library module 1 collects infectious disease samples based on big data and establishes an infectious disease sample library, and the infectious disease sample library module 1 is electrically connected with the infectious disease analysis system 2;
the infectious disease analysis system 2 is used for collecting suspected infectious disease reports, classifying and analyzing the collected suspected infectious disease reports, the infectious disease analysis system 2 comprises an infrastructure module 201 and a report classification module 202, the infrastructure module 201 provides the suspected infectious disease reports based on hospital resources in a system coverage area and detection laboratory resources in the system coverage area, the infrastructure module 201 is electrically connected with the report classification module 202, and the report classification module 202 classifies the suspected infectious disease reports in the infrastructure module 201, analyzes the classified suspected infectious disease reports and outputs an analysis result to the public health response system 3;
the public health response system 3 is used for carrying out emergency response on the command transmitted from the infectious disease analysis system 2, and the public health response system 3 comprises a public emergency response platform 301 and an emergency plan module 302, wherein the public emergency response platform 301 is used for uniformly commanding and dispatching after the infectious disease occurs, and the emergency plan module 302 is used for storing emergency plans formulated for each infectious disease outbreak.
The infectious disease sample library module 1 collects infectious disease samples issued by public departments through big data and models the infectious disease samples, the infectious disease sample library module 1 comprises a sample entry unit 101, a sample modeling unit 102 and a sample library memory 103, the sample entry unit 101 is electrically connected with the sample modeling unit 102, the sample entry unit 101 collects the infectious disease samples and records the infectious disease samples into the sample modeling unit 102, the infectious disease samples recorded by the sample entry unit 101 are nucleic acid sequences of the infectious diseases and antigen data of the corresponding infectious diseases, the data in the infectious disease samples can be obtained through an existing infectious disease database published by the country, an output end of the sample modeling unit 102 is electrically connected with the sample library memory 103, the sample modeling unit 102 is used for establishing infectious sample models for the infectious samples and storing the infectious sample models in the sample library memory 103, namely establishing infectious sample models for the nucleic acid sequences of the infectious diseases and the corresponding antigen data of the infectious diseases, the sample library memory 103 is used for storing the infectious sample models, and the sample library memory 103 is connected with a cloud server.
The infrastructure module 201 comprises a hospital data collection unit 201a, a detection laboratory data collection unit 201b and an emergency report data port 201c, wherein the hospital data collection unit 201a is used for entering a suspected infectious disease report reported in a hospital in a system coverage area, the detection laboratory data collection unit 201b is used for entering a suspected infectious disease report reported in a detection laboratory in the system coverage area, the suspected infectious disease report entered by the detection laboratory data collection unit 201b comprises a nucleic acid sequence of the suspected infectious disease and corresponding antigen data of the suspected infectious disease, and the emergency report data port 201c is used for a suspected infectious disease report reported through a personal port.
The report classification module 202 includes a suspected infectious disease report modeling unit 202a, a primary comparison classification unit 202b, and a secondary comparison classification unit 202c, where the suspected infectious disease report modeling unit 202a is configured to model a suspected infectious disease report from the infrastructure module 201 to form a suspected infectious disease model, and an output end of the suspected infectious disease report modeling unit 202a is electrically connected to the primary comparison classification unit 202b, that is, a nucleic acid sequence of the suspected infectious disease in the suspected infectious disease report and corresponding antigen data of the suspected infectious disease are modeled to form the suspected infectious disease model.
The primary contrast classification unit 202b is configured to compare the suspected infectious disease model established in the suspected infectious disease report modeling unit 202a with the infectious sample model stored in the sample library storage 103, transmit a suspected infectious disease model forming command a to the public health response system 3 if the similarity between the suspected infectious disease model and the infectious sample model is higher than eighty-five percent, and change the suspected infectious disease model into a secondary suspected model if the similarity between the suspected infectious disease model and the infectious sample model is lower than eighty-five percent and transmit the secondary suspected disease model to the secondary contrast classification unit 202 c.
The secondary contrast classification unit 202c receives the secondary suspected models from the primary contrast classification unit 202b, and the secondary contrast classification unit 202c compares the secondary suspected models received within forty-eight hours with each other, and transmits a secondary suspected model forming command b to the public health response system 3 if the similarity between the secondary suspected models is higher than eighty-five percent.
The public emergency response platform 301 comprises a control center unit 301a, a hospital response unit 301b and a local unit response unit 301c, wherein the output end of the control center unit 301a is electrically connected with the hospital response unit 301b and the local unit response unit 301c respectively, the control center unit 301a receives the command a and the command b from the infrastructure module 201 and starts different emergency plans stored in the emergency plan module 302 according to different commands, the hospital response unit 301b is applied to hospitals in system coverage to respond to different emergency plans, and the local unit response unit 301c is applied to local units in the system coverage to respond to different emergency plans.
The local units in the local unit response unit 301c include cities, districts, counties, towns, streets, villages, and natural villages within the coverage of the system.
In summary, when the intelligent medical system capable of preventing infectious diseases based on urban big data is used, firstly, the infectious disease sample library module 1 collects infectious disease samples issued by public departments through big data, and models the infectious disease samples, specifically, the sample entry unit 101 collects the infectious disease samples and enters the sample modeling unit 102, the sample modeling unit 102 establishes infectious sample models for the infectious samples and stores the infectious sample models in the sample library memory 103, and the sample library memory 103 is connected with a cloud server to store the infectious sample models;
the method comprises the steps that suspected infectious disease reports are collected through an infectious disease analysis system 2, the collected suspected infectious disease reports are classified and analyzed, specifically, an infrastructure module 201 provides the suspected infectious disease reports based on hospital resources in a system coverage area and detection laboratory resources in the system coverage area, a report classification module 202 classifies the suspected infectious disease reports in the infrastructure module 201, analyzes the classified suspected infectious disease reports and outputs an analysis result to a public health response system 3, specifically, the suspected infectious disease reports reported in a hospital in the system coverage area are recorded through a hospital data collection unit 201a, the suspected infectious disease reports reported in the detection laboratory in the system coverage area are recorded through a detection laboratory data collection unit 201b, and the suspected infectious disease reports reported through an emergency report data port 201c are reported through a personal port.
The suspected infectious disease report modeling unit 202a is configured to model a suspected infectious disease report from the infrastructure module 201 to form a suspected infectious disease model, the primary comparison classification unit 202b is configured to compare the suspected infectious disease model established in the suspected infectious disease report modeling unit 202a with an infectious sample model stored in the sample library storage 103, transmit a suspected infectious disease model formation command a to the public health response system 3 if the similarity between the suspected infectious disease model and the infectious sample model is higher than eighty-five percent, change the suspected infectious disease model into a secondary suspected model and transmit the secondary suspected model to the secondary comparison classification unit 202c if the similarity between the suspected infectious disease model and the infectious sample model is lower than eighty-five percent, the secondary comparison classification unit 202c receives the secondary suspected model from the primary comparison classification unit 202b, the secondary comparison classification unit 202c receives a mutual comparison between the secondary suspected models within forty-eight hours, and transmits a suspected second secondary model formation command b to the public health response system 3 if the similarity between the suspected infectious disease models is higher than eighty-five percent;
the public health response system 3 is used for carrying out emergency response on commands transmitted from the infectious disease analysis system 2, the public emergency response platform 301 is used for uniformly commanding and dispatching after infectious diseases appear, and the emergency plan module 302 is used for storing emergency plans formulated for each infectious disease outbreak;
specifically, the control center unit 301a receives the command a and the command b from the infrastructure module 201, and starts different emergency plans stored in the emergency plan module 302 according to different commands, the hospital response unit 301b is applied to hospitals in the system coverage to respond to different emergency plans, and the local unit response unit 301c is applied to local units in the system coverage to respond to different emergency plans.
The embodiments of the present invention have been presented for purposes of illustration and description, and are not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.
Claims (8)
1. An intelligent medical system capable of preventing infectious diseases based on urban big data is characterized by comprising an infectious disease sample library module (1), an infectious disease analysis system (2) and a public health response system (3),
the infectious disease sample library module (1) collects infectious disease samples based on big data and establishes an infectious disease sample library, and the infectious disease sample library module (1) is electrically connected with the infectious disease analysis system (2);
the infectious disease analysis system (2) is used for collecting suspected infectious disease reports and classifying and analyzing the collected suspected infectious disease reports, the infectious disease analysis system (2) comprises an infrastructure module (201) and a report classification module (202), the infrastructure module (201) provides the suspected infectious disease reports based on hospital resources in a system coverage area and detection laboratory resources in the system coverage area, the infrastructure module (201) is electrically connected with the report classification module (202), and the report classification module (202) classifies the suspected infectious disease reports in the infrastructure module (201) and analyzes the classified suspected infectious disease reports and outputs an analysis result to the public health response system (3);
the public health response system (3) is used for carrying out emergency response on commands transmitted from the infectious disease analysis system (2), the public health response system (3) comprises a public emergency response platform (301) and an emergency plan module (302), the public emergency response platform (301) is used for unified command and dispatch after infectious diseases appear, and the emergency plan module (302) is used for storing emergency plans formulated for all infectious disease outbreaks.
2. The urban big data based intelligent medical system capable of preventing infectious diseases according to claim 1, wherein the infectious disease sample library module (1) collects infectious disease samples issued by public departments through big data and models the infectious disease samples, the infectious disease sample library module (1) comprises a sample entry unit (101), a sample modeling unit (102) and a sample library memory (103), the sample entry unit (101) is electrically connected with the sample modeling unit (102), the sample entry unit (101) collects and enters the infectious disease samples into the sample modeling unit (102), the output end of the sample modeling unit (102) is electrically connected with the sample library memory (103), the sample modeling unit (102) is used for establishing infectious sample models for the infectious samples and storing the infectious sample models in the sample library memory (103), the sample library memory (103) is used for storing the infectious disease samples, and the sample library memory (103) is connected with a cloud server.
3. The urban big data based intelligent medical system capable of preventing infectious diseases according to claim 1, wherein the infrastructure module (201) comprises a hospital data collection unit (201 a), a detection laboratory data collection unit (201 b) and an emergency report data port (201 c), the hospital data collection unit (201 a) is used for entering suspected infectious disease reports reported in hospitals in the system coverage area, the detection laboratory data collection unit (201 b) is used for entering suspected infectious disease reports reported in detection laboratories in the system coverage area, and the emergency report data port (201 c) is used for suspected infectious disease reports reported through a personal port.
4. The urban big data based intelligent medical system capable of preventing infectious diseases according to claim 2, wherein the report classification module (202) comprises a suspected infectious disease report modeling unit (202 a), a primary contrast classification unit (202 b) and a secondary contrast classification unit (202 c), the suspected infectious disease report modeling unit (202 a) is used for modeling the suspected infectious disease report from the infrastructure module (201) to form a suspected infectious disease model, and an output end of the suspected infectious disease report modeling unit (202 a) is electrically connected with the primary contrast classification unit (202 b).
5. The urban big data based intelligent medical system for preventing infectious diseases according to claim 4, wherein the primary contrast classification unit (202 b) is configured to compare the suspected infectious disease model established in the suspected infectious disease report modeling unit (202 a) with the infectious sample model stored in the sample library storage (103), transmit a suspected infectious disease model forming command a to the public health response system (3) if the similarity between the suspected infectious disease model and the infectious sample model is higher than eighty-five percent, and transmit a suspected infectious disease model modified into a secondary suspected disease model to the secondary contrast classification unit (202 c) if the similarity between the suspected infectious disease model and the infectious sample model is lower than eighty-five percent.
6. The smart medical system for preventing infectious diseases based on big city data as claimed in claim 5, wherein the secondary contrast classification unit (202 c) receives the secondary suspected models from the primary contrast classification unit (202 b), and the secondary contrast classification unit (202 c) compares the secondary suspected models received within forty-eight hours with each other, and transmits a secondary suspected model forming command b to the public health response system (3) if the similarity between the secondary suspected models is higher than eighty-five percent.
7. The intelligent medical system for preventing infectious diseases based on urban big data according to claim 6, wherein the public emergency response platform (301) comprises a control center unit (301 a), a hospital response unit (301 b) and a local unit response unit (301 c), the output end of the control center unit (301 a) is electrically connected with the hospital response unit (301 b) and the local unit response unit (301 c), and the control center unit (301 a) receives the command a and the command b from the infrastructure module (201) and starts different emergency plans stored in the emergency plan module (302) according to different commands, the hospital response unit (301 b) is applied to the hospital within the system coverage to respond to different emergency plans, and the local unit response unit (301 c) is applied to the local unit within the system coverage to respond to different emergency plans.
8. The smart medical system for preventing infectious diseases based on big city data as claimed in claim 7, wherein the local units in the local unit response unit (301 c) include city, district, county, town, street, county and natural village within the system coverage.
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| CN115798734A (en) * | 2023-01-09 | 2023-03-14 | 杭州杏林信息科技有限公司 | New emergent infectious disease prevention and control method and device based on big data and storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013120199A1 (en) * | 2012-02-13 | 2013-08-22 | Kamran Khan | Warning system for infectious diseases and method therefor |
| CN111899893A (en) * | 2020-09-29 | 2020-11-06 | 南京汉卫公共卫生研究院有限公司 | Infectious disease early warning decision platform system |
| CN112115580A (en) * | 2020-08-12 | 2020-12-22 | 科技谷（厦门）信息技术有限公司 | Big data-based newly-released major infectious disease monitoring, early warning and coping system |
| CN112185586A (en) * | 2020-11-17 | 2021-01-05 | 北京嘉和海森健康科技有限公司 | Infectious disease monitoring and early warning method and device |
| CN112466473A (en) * | 2020-09-28 | 2021-03-09 | 北京左医科技有限公司 | Automatic early warning method and system for newly-discovered infectious diseases |
| CN112687399A (en) * | 2020-12-11 | 2021-04-20 | 江苏省人民医院（南京医科大学第一附属医院） | Infectious disease monitoring and early warning system based on artificial intelligence informatization |
| CN113409952A (en) * | 2021-08-20 | 2021-09-17 | 苏州市疾病预防控制中心 | Infectious disease monitoring, prevention and control system and method under multi-point trigger view angle |
| CN114078602A (en) * | 2020-08-19 | 2022-02-22 | 上海市疾病预防控制中心 | System, method and application for realizing rapid tracing, screening and control of key crowds based on big data and AI technology in infectious disease prevention and control |
| CN114373552A (en) * | 2021-11-26 | 2022-04-19 | 南昌协达科技发展有限公司 | Emergency management method, system and equipment for food poisoning and infectious diseases |
-
2022
- 2022-11-23 CN CN202211471962.6A patent/CN115512856A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013120199A1 (en) * | 2012-02-13 | 2013-08-22 | Kamran Khan | Warning system for infectious diseases and method therefor |
| CN112115580A (en) * | 2020-08-12 | 2020-12-22 | 科技谷（厦门）信息技术有限公司 | Big data-based newly-released major infectious disease monitoring, early warning and coping system |
| CN114078602A (en) * | 2020-08-19 | 2022-02-22 | 上海市疾病预防控制中心 | System, method and application for realizing rapid tracing, screening and control of key crowds based on big data and AI technology in infectious disease prevention and control |
| CN112466473A (en) * | 2020-09-28 | 2021-03-09 | 北京左医科技有限公司 | Automatic early warning method and system for newly-discovered infectious diseases |
| CN111899893A (en) * | 2020-09-29 | 2020-11-06 | 南京汉卫公共卫生研究院有限公司 | Infectious disease early warning decision platform system |
| CN112185586A (en) * | 2020-11-17 | 2021-01-05 | 北京嘉和海森健康科技有限公司 | Infectious disease monitoring and early warning method and device |
| CN112687399A (en) * | 2020-12-11 | 2021-04-20 | 江苏省人民医院（南京医科大学第一附属医院） | Infectious disease monitoring and early warning system based on artificial intelligence informatization |
| CN113409952A (en) * | 2021-08-20 | 2021-09-17 | 苏州市疾病预防控制中心 | Infectious disease monitoring, prevention and control system and method under multi-point trigger view angle |
| CN114373552A (en) * | 2021-11-26 | 2022-04-19 | 南昌协达科技发展有限公司 | Emergency management method, system and equipment for food poisoning and infectious diseases |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115798734A (en) * | 2023-01-09 | 2023-03-14 | 杭州杏林信息科技有限公司 | New emergent infectious disease prevention and control method and device based on big data and storage medium |
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