CN111696677B - Information management system for supporting clinical scientific research by using medical big data - Google Patents
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Abstract
The invention discloses an information management system for supporting clinical scientific research by using medical big data, which comprises: medical big data platform, clinical data center, scientific research data center and data operation service center. According to the invention, the medical big data can be classified in a corresponding distributed manner, so that the target file data can be found out quickly according to the requirements, a large amount of arrangement time and searching time can be saved, the distributed file data can be uniformly scheduled according to clinical scientific research requirements, disorder of multi-party scheduling can be avoided, the whole medical big data is orderly in a uniform scheduling manner, and the error utilization rate of the large file data is reduced.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to an information management system for supporting clinical scientific research by using medical big data.
Background
Along with the application of information technologies such as cloud computing, big data, internet of things, mobile interconnection, artificial intelligence and the like in the field of health care, the health care big data are exponentially and rapidly increased, especially by the powerful promotion of the national 'Internet+' and big data strategy. The national and various governments pay more and more attention to promoting the application development of national health informatization and health medical big data, issue a plurality of important files related to medical health in sequence, and make a series of deployments for the health Chinese construction and the application development of the Internet and the medical health and the health medical big data.
The active development of the application of health medical big data has become an important consensus around the world, and some developed countries take it as a major strategy of the country and put it into action. The big data of health care is different from the data of other industries, and the application development of the big data can promote the revolutionary change of the health care mode, thereby being beneficial to expanding the medical resource supply, managing and controlling the medical cost, improving the operation efficiency and quality of medical service and meeting the diversified and multi-level health requirements. The development of data economy is independent of the support of large data of healthy medical treatment, which is beneficial to cultivating new business states and economic growth points, and brings huge business opportunities and entrepreneur space.
At present, cloud computing and big data are made in multiple industries, and data mining has cases and applications in some mature industries, but the medical industry is less. The data type companies in the medical industry mainly adhere to the tradition in the hospital to integrate, interact and fuse data resources, and the sample is single from the aspect of clinical research; in addition, clinical data acquired by scientific research experiments on a medical information system are recorded after various examinations are carried out on patients, certain hysteresis exists in time, and real-time clinical data of the patients cannot be acquired.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, namely, data companies in the medical industry mainly adhere to the tradition in a hospital to realize integration, interaction and fusion of data resources, and the clinical research shows that the sample is single; in addition, clinical data acquired by scientific research experiments on a medical information system are recorded after various examinations are carried out on patients, certain hysteresis exists in time, and real-time clinical data of the patients cannot be acquired. Therefore, the information management system for supporting clinical scientific research by using the medical big data can overcome the problems that medical data of all hospitals in the prior art are scattered and independent and are difficult to integrate and process, and further reduce the difficulty of collecting the medical data; the medical big data is correspondingly distributed and classified, so that different types of distributed file data are obtained, the target file data can be conveniently and quickly found according to the requirements, a large amount of arrangement time and search time can be saved, and the requirements related to the medical treatment are all the requirements of time race originally, so that the part is very important; the distributed file data is uniformly scheduled according to clinical scientific research requirements, so that disorder of multi-party scheduling can be avoided, the whole medical big data is orderly in a uniform scheduling mode, and the error utilization rate of the large file data is reduced; the distributed file data is subjected to off-line calculation processing, so that first calculation data which cannot be directly obtained from the distributed file data is obtained, and the required data is not in the range of the existing distributed file data because the distributed file data in the medical big data is incomplete, and the distributed file data can be obtained only by carrying out relevant logic calculation on the distributed file data, so that the comprehensiveness of the medical big data can be increased by carrying out off-line calculation processing on the distributed file data. The real-time property of the clinical medical data can be ensured by collecting the clinical medical data required by clinical scientific research, compared with the clinical data obtained from a medical information system, the clinical medical data directly collected by the scheme has no delay time, the timeliness is stronger than that of the traditional mode, and the obtained clinical scientific research conclusion is more accurate; the clinical medical data is correspondingly distributed and classified, so that different types of clinical medical data are obtained, the subsequent searching and obtaining of target clinical medical data are facilitated, and the subsequent processing time is saved; the clinical medical data is subjected to off-line calculation processing, so that second calculation data which cannot be directly obtained from the clinical medical data is obtained, similarly, the directly collected clinical medical data is not comprehensive, the required partial data can be directly obtained from the clinical medical data, but the required partial data cannot be obtained from the clinical medical data, and the clinical medical data can be obtained only by carrying out relevant logic calculation, so that the off-line calculation processing is carried out on the clinical medical data, the second calculation data is obtained, and the comprehensiveness of the clinical medical data can be increased. The distributed file data, the first calculation data, the clinical medical data and the second calculation data are cleaned and integrated according to the same category, so that target medical data required by clinical scientific research is obtained, and a doctor of the clinical scientific research can directly use the cleaned and integrated target medical data for clinical scientific research without preliminary processing of the data, thereby saving the time of the clinical scientific research doctor to a certain extent. After the clinical scientific research doctors pass the identity authentication, the large data matched with the self authority can be searched and utilized, and the related large data can be effectively mined according to the understanding of the self on the searched large data, namely, the current target medical data can be found to be calculated in a related manner, and the large data mining is carried out and the mining medical data is obtained when the mining medical data which is different from the target medical data and is beneficial to the clinical scientific research is obtained; provides target medical data and mining medical data visualization services for clinical scientists and provides operation guidelines for the clinical scientists.
The aim of the invention is realized by the following technical scheme:
an information management system for supporting clinical research using medical big data, comprising:
the system comprises a medical big data platform, a clinical data center, a scientific research data center and a data operation service center; wherein,,
the medical big data platform comprises:
the medical big data access module is used for providing medical big data for clinical scientific research;
the distributed file module is used for carrying out corresponding distributed classification on the medical big data so as to obtain distributed file data of different categories;
the distributed database module is used for storing the distributed file data into a distributed database of a corresponding category;
the uniform resource scheduling module is used for uniformly scheduling the distributed file data according to clinical scientific research requirements;
the first offline computing module is used for performing offline computing processing on the distributed file data so as to obtain first computing data which cannot be directly obtained from the distributed file data;
the clinical data center includes:
the clinical data acquisition module is used for acquiring clinical medical data required by clinical scientific research;
the clinical data classification module is used for carrying out corresponding distributed classification on the clinical medical data so as to obtain clinical medical data of different categories;
A clinical data storage module for storing different categories of the clinical medical data;
the second off-line computing module is used for performing off-line computing processing on the clinical medical data so as to obtain second computing data which cannot be directly obtained from the clinical medical data;
the scientific research data center comprises:
the scientific research data acquisition module is used for the distributed file data, the first calculation data, the clinical medical data and the second calculation data;
the data integration module is used for cleaning the distributed file data, the first calculation data, the clinical medical data and the second calculation data and integrating the distributed file data, the first calculation data, the clinical medical data and the second calculation data according to the same category so as to obtain target medical data required by clinical scientific research;
the data operation service center includes:
the doctor identity authentication module is used for authenticating the true identity of the clinical scientific research doctor;
the permission configuration module is used for configuring corresponding operation permissions according to the identities of the clinical scientific research doctors passing the authentication;
the big data searching module is used for searching target medical data which are required by clinical scientific research doctors and have operation authority;
the large data mining module is used for carrying out large data mining and obtaining mining medical data when a clinical scientific research doctor obtains corresponding target medical data, and the current target medical data can be calculated in a correlated way if found, and the mining medical data which is different from the target medical data and is beneficial to clinical scientific research is obtained;
The big data visualization module is used for visualizing the target medical data and the mining medical data;
and the big data guide module is used for providing an operation guide for clinical scientific researchers.
Preferably, the medical big data access module acquires medical big data from all medical information systems in the internet, wherein the medical information systems at least comprise a family medical information system, a community medical information system, a nursing home medical information system, a hospital medical information system, a physical examination institution medical information system, a medical scientific research medical information system and a medical education training medical information system.
Preferably, the first offline computing module is further configured to perform error data identification and deletion on the distributed file data, specifically:
acquiring the distributed file data of the same patient, wherein one group of the distributed file data comprises medical record data, diagnosis data, medical advice data, treatment data, rehabilitation data and physical examination data;
and identifying and removing error data in the medical record data according to the data corresponding relation among the medical record data, the diagnosis data, the medical advice data, the treatment data, the rehabilitation data and the physical examination data.
Preferably, the data correspondence is obtained by adopting a deep learning mode based on feature fusion, and specifically comprises the following steps:
taking all the distributed file data of the same patient as a sample set;
taking one part of the sample set as a training set and the other part as a test set;
in the training set, after the medical record data, the diagnosis data, the medical advice data and the physical examination data are subjected to feature fusion, the medical record data, the diagnosis data, the medical advice data and the physical examination data are used as input of a deep learning model, and after the treatment data and the rehabilitation data are subjected to feature fusion, the medical record data, the diagnosis data, the medical advice data and the physical examination data are used as output of the deep learning model, so that the deep learning model is built;
in the test set, after the medical record data, the diagnosis data, the medical advice data and the physical examination data are subjected to feature fusion, the medical record data, the diagnosis data, the medical advice data and the physical examination data are used as input of the deep learning model, the treatment data and the rehabilitation data are subjected to feature fusion, the medical record data, the diagnosis data, the medical advice data and the physical examination data are used as output of the deep learning model, and a target deep learning model is obtained after a test result accords with a standard;
and when error data identification and deletion are carried out, the medical record data, diagnosis data, medical advice data and physical examination data in the set of distributed file data which need to be identified are subjected to feature fusion and then are used as input of the target deep learning model, so that standard treatment data and rehabilitation data are obtained, the standard treatment data and the standard rehabilitation data are compared and judged with the treatment data and the rehabilitation data in the set of distributed file data, if the standard treatment data and the rehabilitation data are not matched with the treatment data and the rehabilitation data in the set of distributed file data, the set of distributed file data is identified as error data, and the set of error data is deleted.
Preferably, the clinical data acquisition module is internally integrated with a common patient identification system and a first alarm system, wherein the common patient identification system comprises a fingerprint identification unit, an identity card verification unit, a face identification unit and an iris identification unit;
the fingerprint identification unit is used for reading and identifying real-time fingerprint information of a patient, and sending an alarm driving instruction to the first alarm system when the real-time fingerprint information is not matched with the historical fingerprint information of the patient;
the identity card verification unit is used for reading and identifying real-time identity card information of a patient, and when the real-time identity card information is not matched with historical identity card information of the patient, the identity card verification unit sends an alarm driving instruction to the first alarm system;
the face recognition unit is used for collecting and recognizing real-time face information of a patient, and when the real-time face information is not matched with the historical face information of the patient, the face recognition unit sends an alarm driving instruction to the first alarm system;
the iris recognition unit is used for collecting and recognizing real-time iris information of a patient, and sending an alarm driving instruction to the first alarm system when the real-time iris information is not matched with the historical iris information of the patient;
The first alarm system is used for sending out primary alarm to clinical medical data acquisition staff.
Preferably, the clinical data acquisition module is internally integrated with a standby patient identification system and a second alarm system; the standby patient identification system comprises a tone color identification unit, a weight identification unit and a height identification unit;
the tone color identification unit is used for acquiring and identifying real-time tone color information of a patient, and sending an alarm driving instruction to the first alarm system when the real-time tone color information is not matched with the historical tone color information of the patient;
the weight identification unit is used for acquiring and identifying real-time weight information of a patient, and sending an alarm driving instruction to the first alarm system when the real-time weight information is not matched with the historical weight information of the patient;
the height identification unit is used for acquiring and identifying real-time height information of a patient, and sending an alarm driving instruction to the first alarm system when the real-time height information is not matched with the historical height information of the patient;
when the second alarm system receives the alarm driving instruction of two or more of the tone color recognition unit, the weight recognition unit and the height recognition unit, the second alarm system sends secondary alarms to the clinical medical data acquisition staff in a secret mode.
Preferably, the clinical data acquisition module further comprises a mobile clinical data acquisition module arranged at the residence of the patient, and the mobile clinical data acquisition module is used for acquiring clinical medical data of the patient inconvenient to move.
Preferably, the mobile clinical data acquisition module is used for acquiring electroencephalogram detection data, heart rate detection data, blood detection data, pulse detection data, body temperature detection data, electrocardiograph detection data, respiration detection data, symptom detection data and patient self-input data of a patient.
The beneficial effects of the invention are as follows: 1. the problems that medical data of all hospitals are scattered and independent and are difficult to integrate and process in the prior art can be solved, and the difficulty of collecting the medical data is further reduced; the medical big data is correspondingly distributed and classified, so that different types of distributed file data are obtained, the target file data can be conveniently and quickly found according to the requirements, a large amount of arrangement time and search time can be saved, and the requirements related to the medical treatment are all the requirements of time race originally, so that the part is very important; the distributed file data is uniformly scheduled according to clinical scientific research requirements, so that disorder of multi-party scheduling can be avoided, the whole medical big data is orderly in a uniform scheduling mode, and the error utilization rate of the large file data is reduced; the distributed file data is subjected to off-line calculation processing, so that first calculation data which cannot be directly obtained from the distributed file data is obtained, and the required data is not in the range of the existing distributed file data because the distributed file data in the medical big data is incomplete, and the distributed file data can be obtained only by carrying out relevant logic calculation on the distributed file data, so that the comprehensiveness of the medical big data can be increased by carrying out off-line calculation processing on the distributed file data.
2. The real-time property of the clinical medical data can be ensured by collecting the clinical medical data required by clinical scientific research, compared with the clinical data obtained from a medical information system, the clinical medical data directly collected by the scheme has no delay time, the timeliness is stronger than that of the traditional mode, and the obtained clinical scientific research conclusion is more accurate; the clinical medical data is correspondingly distributed and classified, so that different types of clinical medical data are obtained, the subsequent searching and obtaining of target clinical medical data are facilitated, and the subsequent processing time is saved; the clinical medical data is subjected to off-line calculation processing, so that second calculation data which cannot be directly obtained from the clinical medical data is obtained, similarly, the directly collected clinical medical data is not comprehensive, the required partial data can be directly obtained from the clinical medical data, but the required partial data cannot be obtained from the clinical medical data, and the clinical medical data can be obtained only by carrying out relevant logic calculation, so that the off-line calculation processing is carried out on the clinical medical data, the second calculation data is obtained, and the comprehensiveness of the clinical medical data can be increased.
3. The distributed file data, the first calculation data, the clinical medical data and the second calculation data are cleaned and integrated according to the same category, so that target medical data required by clinical scientific research is obtained, and a doctor of the clinical scientific research can directly use the cleaned and integrated target medical data for clinical scientific research without preliminary processing of the data, thereby saving the time of the clinical scientific research doctor to a certain extent.
4. After the clinical scientific research doctors pass the identity authentication, the large data matched with the self authority can be searched and utilized, and the related large data can be effectively mined according to the understanding of the self on the searched large data, namely, the current target medical data can be found to be calculated in a related manner, and the large data mining is carried out and the mining medical data is obtained when the mining medical data which is different from the target medical data and is beneficial to the clinical scientific research is obtained; provides target medical data and mining medical data visualization services for clinical scientists and provides operation guidelines for the clinical scientists.
Drawings
FIG. 1 is a block diagram of one embodiment of the present invention;
FIG. 2 is a schematic diagram of a medical information system according to an embodiment of the present invention;
FIG. 3 is a flow chart illustrating steps of error data identification and deletion according to an embodiment of the present invention.
Fig. 4 is a flowchart illustrating a data correspondence determination step according to an embodiment of the present invention.
Detailed Description
The technical solution of the present invention will be described in further detail with reference to the accompanying drawings, but the scope of the present invention is not limited to the following description.
Examples:
aiming at the prior art, the data type companies in the medical industry mainly adhere to the tradition in the open hospital to realize integration, interaction and fusion of data resources, and the clinical research is seen as single sample; in addition, clinical data acquired by scientific research experiments on a medical information system are recorded after various examinations are carried out on patients, certain hysteresis exists in time, and real-time clinical data of the patients cannot be acquired.
As shown in fig. 1, an information management system for supporting clinical research using medical big data, comprising:
the system comprises a medical big data platform, a clinical data center, a scientific research data center and a data operation service center; wherein,,
the medical big data platform comprises:
the medical big data access module is used for providing medical big data for clinical scientific research;
the distributed file module is used for carrying out corresponding distributed classification on the medical big data so as to obtain distributed file data of different categories;
the distributed database module is used for storing the distributed file data into a distributed database of a corresponding category;
the uniform resource scheduling module is used for uniformly scheduling the distributed file data according to clinical scientific research requirements;
the first offline computing module is used for performing offline computing processing on the distributed file data so as to obtain first computing data which cannot be directly obtained from the distributed file data;
for a medical big data platform, the problems that medical data of all hospitals in the prior art are scattered and independent and are difficult to integrate and process can be solved, and the difficulty of collecting the medical data is further reduced; the medical big data is correspondingly distributed and classified, so that different types of distributed file data are obtained, the target file data can be conveniently and quickly found according to the requirements, a large amount of arrangement time and search time can be saved, and the requirements related to the medical treatment are all the requirements of time race originally, so that the part is very important; the distributed file data is uniformly scheduled according to clinical scientific research requirements, so that disorder of multi-party scheduling can be avoided, the whole medical big data is orderly in a uniform scheduling mode, and the error utilization rate of the large file data is reduced; the distributed file data is subjected to off-line calculation processing, so that first calculation data which cannot be directly obtained from the distributed file data is obtained, and the required data is not in the range of the existing distributed file data because the distributed file data in the medical big data is incomplete, and the distributed file data can be obtained only by carrying out relevant logic calculation on the distributed file data, so that the comprehensiveness of the medical big data can be increased by carrying out off-line calculation processing on the distributed file data.
The clinical data center includes:
the clinical data acquisition module is used for acquiring clinical medical data required by clinical scientific research;
the clinical data classification module is used for carrying out corresponding distributed classification on the clinical medical data so as to obtain clinical medical data of different categories;
a clinical data storage module for storing different categories of the clinical medical data;
the second off-line computing module is used for performing off-line computing processing on the clinical medical data so as to obtain second computing data which cannot be directly obtained from the clinical medical data;
for a clinical data center, the real-time property of the clinical medical data can be ensured by collecting the clinical medical data required by clinical scientific research, and compared with the clinical data obtained from a medical information system, the clinical medical data directly collected by the scheme has no delay time, has higher timeliness than that of the traditional mode, and can obtain more accurate clinical scientific research conclusion; the clinical medical data is correspondingly distributed and classified, so that different types of clinical medical data are obtained, the subsequent searching and obtaining of target clinical medical data are facilitated, and the subsequent processing time is saved; the clinical medical data is subjected to off-line calculation processing, so that second calculation data which cannot be directly obtained from the clinical medical data is obtained, similarly, the directly collected clinical medical data is not comprehensive, the required partial data can be directly obtained from the clinical medical data, but the required partial data cannot be obtained from the clinical medical data, and the clinical medical data can be obtained only by carrying out relevant logic calculation, so that the off-line calculation processing is carried out on the clinical medical data, the second calculation data is obtained, and the comprehensiveness of the clinical medical data can be increased.
The scientific research data center comprises:
the scientific research data acquisition module is used for the distributed file data, the first calculation data, the clinical medical data and the second calculation data;
the data integration module is used for cleaning the distributed file data, the first calculation data, the clinical medical data and the second calculation data and integrating the distributed file data, the first calculation data, the clinical medical data and the second calculation data according to the same category so as to obtain target medical data required by clinical scientific research;
for a scientific research data center, the distributed file data, the first calculation data, the clinical medical data and the second calculation data are cleaned and integrated according to the same category, so that target medical data required by clinical scientific research is obtained, and a doctor in clinical scientific research can directly use the cleaned and integrated target medical data in clinical scientific research without preliminary processing of the data, thereby saving time of a clinical scientific research doctor to a certain extent.
The data operation service center includes:
the doctor identity authentication module is used for authenticating the true identity of the clinical scientific research doctor;
the permission configuration module is used for configuring corresponding operation permissions according to the identities of the clinical scientific research doctors passing the authentication;
The big data searching module is used for searching target medical data which are required by clinical scientific research doctors and have operation authority;
the large data mining module is used for carrying out large data mining and obtaining mining medical data when a clinical scientific research doctor obtains corresponding target medical data, and the current target medical data can be calculated in a correlated way if found, and the mining medical data which is different from the target medical data and is beneficial to clinical scientific research is obtained;
the big data visualization module is used for visualizing the target medical data and the mining medical data;
and the big data guide module is used for providing an operation guide for clinical scientific researchers.
For the data operation service center, after the identity authentication, a clinical scientific research doctor can search and utilize big data matched with own authority, and can also effectively perform relevant big data mining according to the understanding of the searched big data, namely, the current target medical data can be found to perform relevant reckoning, and when mining medical data which is different from the target medical data and is beneficial to clinical scientific research is obtained, big data mining is performed and mining medical data is obtained; provides target medical data and mining medical data visualization services for clinical scientists and provides operation guidelines for the clinical scientists.
Aiming at the prior art, the data type companies in the medical industry mainly adhere to the tradition in the open hospital, realize the integration, interaction and fusion of data resources, and have single sample from the aspect of clinical research. The method integrates, interacts and fuses all medical data in the target area.
As shown in fig. 2, preferably, the medical big data access module acquires medical big data from all medical information systems in the internet, wherein the medical information systems at least include a home medical information system, a community medical information system, a nursing home medical information system, a hospital medical information system, a physical examination institution medical information system, a medical scientific research medical information system, and a medical education training medical information system.
For the distributed file data in the above scheme, because there are error cases in the distributed file data, and the error distributed file data cannot be provided to the clinical scientists, it is necessary to identify and delete the error data before providing the data to the clinical scientists.
As shown in fig. 3, preferably, the first offline computing module is further configured to perform error data identification and deletion on the distributed file data, and specifically is:
Acquiring the distributed file data of the same patient, wherein one group of the distributed file data comprises medical record data, diagnosis data, medical advice data, treatment data, rehabilitation data and physical examination data;
and identifying and removing error data in the medical record data according to the data corresponding relation among the medical record data, the diagnosis data, the medical advice data, the treatment data, the rehabilitation data and the physical examination data.
The scheme utilizes the regularity of the distributed file data of the same patient to provide the regularity judgment for the medical record data, the diagnosis data, the doctor's advice data, the treatment data, the rehabilitation data and the physical examination data, and is favorable for providing judgment accuracy; by adopting a single factor judgment mode, judgment errors are easy to cause, and effective and correct data loss is possibly caused.
Aiming at the regularity judgment, the scheme utilizes the principle of deep learning, and designs a target deep learning model attached to the scheme to judge the correctness of data.
As shown in fig. 4, preferably, the data correspondence is obtained by adopting a deep learning mode based on feature fusion, which specifically includes:
taking all the distributed file data of the same patient as a sample set;
Taking one part of the sample set as a training set and the other part as a test set;
in the training set, after the medical record data, the diagnosis data, the medical advice data and the physical examination data are subjected to feature fusion, the medical record data, the diagnosis data, the medical advice data and the physical examination data are used as input of a deep learning model, and after the treatment data and the rehabilitation data are subjected to feature fusion, the medical record data, the diagnosis data, the medical advice data and the physical examination data are used as output of the deep learning model, so that the deep learning model is built;
in the test set, after the medical record data, the diagnosis data, the medical advice data and the physical examination data are subjected to feature fusion, the medical record data, the diagnosis data, the medical advice data and the physical examination data are used as input of the deep learning model, the treatment data and the rehabilitation data are subjected to feature fusion, the medical record data, the diagnosis data, the medical advice data and the physical examination data are used as output of the deep learning model, and a target deep learning model is obtained after a test result accords with a standard;
and when error data identification and deletion are carried out, the medical record data, diagnosis data, medical advice data and physical examination data in the set of distributed file data which need to be identified are subjected to feature fusion and then are used as input of the target deep learning model, so that standard treatment data and rehabilitation data are obtained, the standard treatment data and the standard rehabilitation data are compared and judged with the treatment data and the rehabilitation data in the set of distributed file data, if the standard treatment data and the rehabilitation data are not matched with the treatment data and the rehabilitation data in the set of distributed file data, the set of distributed file data is identified as error data, and the set of error data is deleted.
In the scheme, firstly, a multi-input and multi-output mode is adopted for training and testing, so that errors are reduced, and the accuracy of data prediction is improved; secondly, fusing a plurality of input factors, rather than inputting the plurality of input factors into a model respectively, so that the error is further reduced; and finally, the output result is two standard treatment data and rehabilitation data, the standard treatment data and rehabilitation data are compared with the treatment data and rehabilitation data in the distributed file data, the data comparison and inspection are ensured, the error data are accurately judged, the corresponding error data are deleted, and the accuracy of the data is ensured.
For the clinical data acquisition module, the authenticity of the clinical data acquired by the patient needs to be ensured, so that the real identity of the patient needs to be identified and judged, and the patient is prevented from making a false for the clinical data. Particularly, when a disease with a high risk is encountered, such as a respiratory disease with high infectivity, high medical cost, limitation of freedom of patients, and the like, some patients often have the situation of falsification to avoid trouble of getting themselves, and then the situation is of serious interest to the public.
The innovation point of the scheme is that, preferably, the clinical data acquisition module is internally integrated with a common patient identity recognition system and a first alarm system, wherein the common patient identity recognition system comprises a fingerprint recognition unit, an identity card verification unit, a face recognition unit and an iris recognition unit;
the fingerprint identification unit is used for reading and identifying real-time fingerprint information of a patient, and sending an alarm driving instruction to the first alarm system when the real-time fingerprint information is not matched with the historical fingerprint information of the patient;
the identity card verification unit is used for reading and identifying real-time identity card information of a patient, and when the real-time identity card information is not matched with historical identity card information of the patient, the identity card verification unit sends an alarm driving instruction to the first alarm system;
the face recognition unit is used for collecting and recognizing real-time face information of a patient, and when the real-time face information is not matched with the historical face information of the patient, the face recognition unit sends an alarm driving instruction to the first alarm system;
the iris recognition unit is used for collecting and recognizing real-time iris information of a patient, and sending an alarm driving instruction to the first alarm system when the real-time iris information is not matched with the historical iris information of the patient;
The first alarm system is used for sending out primary alarm to clinical medical data acquisition staff.
The identity of the patient is verified by adopting various high-tech identity verification steps, so that the authenticity of the identity of the patient can be basically ensured; although the probability that the face information and the iris information can be falsified is small, the cost is high, the patient is not saved, the falsification is as good as in a common patient identification system for self benefit, and a few scientific products with extremely high cost are adopted to overcome face recognition and iris recognition, so that in the case, the scheme also designs a standby patient identification system which is the secondary identification performed without the knowledge of the patient; the collected clinical scientific research data can be completely ensured to be the data of the patient.
One innovation point of the scheme is that, preferably, a standby patient identification system and a second alarm system are integrated in the clinical data acquisition module; the standby patient identification system comprises a tone color identification unit, a weight identification unit and a height identification unit;
the tone color identification unit is used for acquiring and identifying real-time tone color information of a patient, and sending an alarm driving instruction to the first alarm system when the real-time tone color information is not matched with the historical tone color information of the patient;
The weight identification unit is used for acquiring and identifying real-time weight information of a patient, and sending an alarm driving instruction to the first alarm system when the real-time weight information is not matched with the historical weight information of the patient;
the height identification unit is used for acquiring and identifying real-time height information of a patient, and sending an alarm driving instruction to the first alarm system when the real-time height information is not matched with the historical height information of the patient;
when the second alarm system receives the alarm driving instruction of two or more of the tone color recognition unit, the weight recognition unit and the height recognition unit, the second alarm system sends secondary alarms to the clinical medical data acquisition staff in a secret mode.
When the second alarm system only sends secondary alarm to the clinical medical data acquisition staff, it indicates that one item of data is abnormal and needs to be checked in time by the clinical medical data acquisition staff, but when the second alarm system sends secondary alarm to the clinical medical data acquisition staff and the guard staff, it indicates that two or three items of data are abnormal and the patient is in a false situation, so that alarm information needs to be sent to the guard staff at the same time, and false patients can be found in time by adopting an emergency response mode, thereby ensuring the correctness of clinical scientific research data and even guaranteeing the benefit of the public.
For patients who are inconvenient to go out, a mobile clinical data acquisition module is provided for patients who need to acquire clinical data at home, such as during epidemic situations of large infectious diseases, and acquired clinical medical data can be transmitted to the data integration module through remote data transmission.
Preferably, the clinical data acquisition module further comprises a mobile clinical data acquisition module arranged at the residence of the patient, and the mobile clinical data acquisition module is used for acquiring clinical medical data of the patient inconvenient to move.
Preferably, the mobile clinical data acquisition module is used for acquiring electroencephalogram detection data, heart rate detection data, blood detection data, pulse detection data, body temperature detection data, electrocardiograph detection data, respiration detection data, symptom detection data and patient self-input data of a patient.
The foregoing is merely a preferred embodiment of the invention, and it is to be understood that the invention is not limited to the form disclosed herein but is not to be construed as excluding other embodiments, but is capable of numerous other combinations, modifications and environments and is capable of modifications within the scope of the inventive concept, either as taught or as a matter of routine skill or knowledge in the relevant art. And that modifications and variations which do not depart from the spirit and scope of the invention are intended to be within the scope of the appended claims.
Claims (8)
1. An information management system for supporting clinical research using medical big data, comprising:
the system comprises a medical big data platform, a clinical data center, a scientific research data center and a data operation service center; wherein,,
the medical big data platform comprises:
the medical big data access module is used for providing medical big data for clinical scientific research;
the distributed file module is used for carrying out corresponding distributed classification on the medical big data so as to obtain distributed file data of different categories;
the distributed database module is used for storing the distributed file data into a distributed database of a corresponding category;
the uniform resource scheduling module is used for uniformly scheduling the distributed file data according to clinical scientific research requirements;
the first offline computing module is used for performing offline computing processing on the distributed file data so as to obtain first computing data which cannot be directly obtained from the distributed file data;
the clinical data center includes:
the clinical data acquisition module is used for acquiring clinical medical data required by clinical scientific research;
the clinical data classification module is used for carrying out corresponding distributed classification on the clinical medical data so as to obtain clinical medical data of different categories;
A clinical data storage module for storing different categories of the clinical medical data;
the second off-line computing module is used for performing off-line computing processing on the clinical medical data so as to obtain second computing data which cannot be directly obtained from the clinical medical data;
the scientific research data center comprises:
the scientific research data acquisition module is used for acquiring the distributed file data, the first calculation data, the clinical medical data and the second calculation data;
the data integration module is used for cleaning the distributed file data, the first calculation data, the clinical medical data and the second calculation data and integrating the distributed file data, the first calculation data, the clinical medical data and the second calculation data according to the same category so as to obtain target medical data required by clinical scientific research;
the data operation service center includes:
the doctor identity authentication module is used for authenticating the true identity of the clinical scientific research doctor;
the permission configuration module is used for configuring corresponding operation permissions according to the identities of the clinical scientific research doctors passing the authentication;
the big data searching module is used for searching target medical data which are required by clinical scientific research doctors and have operation authority;
the large data mining module is used for carrying out large data mining and obtaining mining medical data when a clinical scientific research doctor obtains corresponding target medical data, and the current target medical data can be calculated in a correlated way if found, and the mining medical data which is different from the target medical data and is beneficial to clinical scientific research is obtained;
The big data visualization module is used for visualizing the target medical data and the mining medical data;
and the big data guide module is used for providing an operation guide for clinical scientific researchers.
2. The information management system for supporting clinical studies using medical big data according to claim 1, wherein the medical big data access module acquires medical big data from all medical information systems in the internet, wherein the medical information systems at least include a home medical information system, a community medical information system, a nursing home medical information system, a hospital medical information system, a physical examination institution medical information system, a medical research medical information system, a medical education training medical information system.
3. The information management system for supporting clinical research with big medical data according to claim 1, wherein the first offline computing module is further configured to perform error data identification and deletion on the distributed file data, specifically:
acquiring the distributed file data of the same patient, wherein one group of the distributed file data comprises medical record data, diagnosis data, medical advice data, treatment data, rehabilitation data and physical examination data;
And identifying and removing error data in the medical record data according to the data corresponding relation among the medical record data, the diagnosis data, the medical advice data, the treatment data, the rehabilitation data and the physical examination data.
4. The information management system for supporting clinical scientific research by using medical big data according to claim 3, wherein the data correspondence is obtained by adopting a deep learning mode based on feature fusion, specifically:
taking all the distributed file data of the same patient as a sample set;
taking one part of the sample set as a training set and the other part as a test set;
in the training set, after the medical record data, the diagnosis data, the medical advice data and the physical examination data are subjected to feature fusion, the medical record data, the diagnosis data, the medical advice data and the physical examination data are used as input of a deep learning model, and after the treatment data and the rehabilitation data are subjected to feature fusion, the medical record data, the diagnosis data, the medical advice data and the physical examination data are used as output of the deep learning model, so that the deep learning model is built;
in the test set, after the medical record data, the diagnosis data, the medical advice data and the physical examination data are subjected to feature fusion, the medical record data, the diagnosis data, the medical advice data and the physical examination data are used as input of the deep learning model, the treatment data and the rehabilitation data are subjected to feature fusion, the medical record data, the diagnosis data, the medical advice data and the physical examination data are used as output of the deep learning model, and a target deep learning model is obtained after a test result accords with a standard;
And when error data identification and deletion are carried out, the medical record data, diagnosis data, medical advice data and physical examination data in the set of distributed file data which need to be identified are subjected to feature fusion and then are used as input of the target deep learning model, so that standard treatment data and rehabilitation data are obtained, the standard treatment data and the standard rehabilitation data are compared and judged with the treatment data and the rehabilitation data in the set of distributed file data, if the standard treatment data and the rehabilitation data are not matched with the treatment data and the rehabilitation data in the set of distributed file data, the set of distributed file data is identified as error data, and the set of error data is deleted.
5. The information management system for supporting clinical scientific research by using medical big data according to claim 1, wherein the clinical data acquisition module is internally integrated with a common patient identification system and a first alarm system, and the common patient identification system comprises a fingerprint identification unit, an identification card verification unit, a face identification unit and an iris identification unit;
the fingerprint identification unit is used for reading and identifying real-time fingerprint information of a patient, and sending an alarm driving instruction to the first alarm system when the real-time fingerprint information is not matched with the historical fingerprint information of the patient;
The identity card verification unit is used for reading and identifying real-time identity card information of a patient, and when the real-time identity card information is not matched with historical identity card information of the patient, the identity card verification unit sends an alarm driving instruction to the first alarm system;
the face recognition unit is used for collecting and recognizing real-time face information of a patient, and when the real-time face information is not matched with the historical face information of the patient, the face recognition unit sends an alarm driving instruction to the first alarm system;
the iris recognition unit is used for collecting and recognizing real-time iris information of a patient, and sending an alarm driving instruction to the first alarm system when the real-time iris information is not matched with the historical iris information of the patient;
the first alarm system is used for sending out primary alarm to clinical medical data acquisition staff.
6. The information management system for supporting clinical research by using medical big data according to claim 5, wherein the clinical data acquisition module is internally integrated with a standby patient identification system and a second alarm system; the standby patient identification system comprises a tone color identification unit, a weight identification unit and a height identification unit;
The tone color identification unit is used for acquiring and identifying real-time tone color information of a patient, and sending an alarm driving instruction to the first alarm system when the real-time tone color information is not matched with the historical tone color information of the patient;
the weight identification unit is used for acquiring and identifying real-time weight information of a patient, and sending an alarm driving instruction to the first alarm system when the real-time weight information is not matched with the historical weight information of the patient;
the height identification unit is used for acquiring and identifying real-time height information of a patient, and sending an alarm driving instruction to the first alarm system when the real-time height information is not matched with the historical height information of the patient;
when the second alarm system receives the alarm driving instruction of two or more of the tone color recognition unit, the weight recognition unit and the height recognition unit, the second alarm system sends secondary alarms to the clinical medical data acquisition staff in a secret mode.
7. The information management system for supporting clinical research using medical big data according to claim 6, wherein the clinical data acquisition module further comprises a mobile clinical data acquisition module provided at a residence of the patient for acquiring clinical medical data of the patient inconvenient to move.
8. The information management system for supporting clinical research with big medical data according to claim 7, wherein the mobile clinical data acquisition module is used for acquiring electroencephalogram detection data, heart rate detection data, blood detection data, pulse detection data, body temperature detection data, electrocardiographic detection data, respiration detection data, symptom detection data and patient's own input data of the patient.
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