CN111696677A - 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: the system comprises a medical big data platform, a clinical data center, a scientific research data center and a data operation service center. According to the method, the medical big data can be classified in a corresponding distributed mode, the target file data can be conveniently and rapidly found according to requirements subsequently, a large amount of arrangement time and searching time can be saved, the distributed file data are uniformly scheduled according to clinical scientific research requirements, the disorder situation of multi-party scheduling can be avoided, the whole medical big data is orderly scheduled by adopting a uniform scheduling mode, and the error utilization rate of the file data is reduced greatly.
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
With the application of information technologies such as cloud computing, big data, internet of things, mobile internet and artificial intelligence in the health and medical field, particularly the powerful promotion of national internet plus and big data strategy, the health and medical big data is increased exponentially. Governments in China and various regions pay more and more attention to promote the informatization of the health of the whole people and the application development of health medical big data, a plurality of important documents related to the aspect of medical health are issued successively, and a series of deployments are made aiming at the construction of healthy China and the application development of Internet + medical health and the health medical big data.
Active development and application of health and medical data has become an important consensus in countries of the world, and some developed countries use it as a national strategy and act. The health medical big data is different from data of other industries, the application development of the health medical big data can promote revolutionary change of a health medical mode, medical resource supply can be favorably expanded, medical cost can be favorably controlled, the operating efficiency and quality of medical services can be improved, and diversified and multi-level health requirements can be met. The development of data economy does not leave the support of big data of health care, which is beneficial to cultivating new state and economic growth points and brings huge business opportunities and entrepreneurship spaces.
At present, cloud computing and big data are performed in multiple industries, cases and applications of data mining are already found in some mature industries, but the medical industry is less. Data type companies in the medical industry mainly insist on tradition in a hospital to realize integration, interaction and fusion of data resources, and from the perspective of clinical research, a sample is single; moreover, clinical data acquired by the scientific research test on the medical information system are input after various examinations are performed 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 type companies in the medical industry mainly insist on the tradition of a communicating hospital, the integration, interaction and fusion of data resources are realized, and from the clinical research, the sample is single; moreover, clinical data acquired by the scientific research test on the medical information system are input after various examinations are performed 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 medical big data is provided, the problems that medical data of hospitals are dispersed and independent and are difficult to integrate and process in the prior art can be solved, and the difficulty in collecting the medical data is further reduced; the medical big data are subjected to corresponding distributed classification, so that distributed file data of different categories are obtained, the target file data can be conveniently and quickly found according to requirements in the follow-up process, a large amount of sorting time and searching time can be saved, and requirements related to medical treatment are requirements for time racing, so that the parts are very important; the distributed file data are uniformly scheduled according to the clinical scientific research requirements, so that the condition of disorder in multi-party scheduling can be avoided, the whole medical big data are orderly by adopting a uniform scheduling mode, and the error utilization rate of the file data is reduced; the distributed file data is subjected to offline calculation processing, so that first calculation data which cannot be directly obtained from the distributed file data is obtained, and the comprehensiveness of the medical big data can be increased by performing offline calculation processing on the distributed file data because the distributed file data in the medical big data is incomplete, required data is not in the existing distributed file data range, and the required data can be obtained only by performing related logic calculation on the distributed file data. Clinical medical data required by clinical scientific research are collected, the real-time performance of the clinical medical data can be guaranteed, 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 are classified in a corresponding distributed mode, so that different types of clinical medical data are obtained, the target clinical medical data can be conveniently searched and obtained in the follow-up process, and the follow-up processing time is saved; the clinical medical data are processed by off-line calculation, so that second calculation data which cannot be directly obtained from the clinical medical data are obtained, similarly, the directly acquired clinical medical data are not comprehensive, required partial data can be directly obtained from the clinical medical data, but required partial data cannot be obtained from the required partial data, and the required partial data can be obtained by carrying out related logic calculation, so that the second calculation data are obtained by off-line calculation on the clinical medical data, 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 are obtained, and doctors in clinical scientific research can directly obtain the cleaned and integrated target medical data and can directly use the data in clinical scientific research without performing primary processing on the data, so that the time of the doctors in clinical scientific research is saved to a certain extent. After passing identity authentication, clinical scientific research doctors can search and utilize big data matched with their own authority, and can effectively mine relevant big data according to the self understanding of the searched big data, namely, relevant calculation can be carried out on the current target medical data, and when the mined medical data which is different from the target medical data and is beneficial to clinical scientific research is obtained, big data mining is carried out to obtain the mined medical data; the method provides target medical data and visual service for mining medical data for clinical scientific researchers, and provides operation guidance for the clinical scientific researchers.
The purpose of the invention is realized by the following technical scheme:
utilize medical treatment big data to support clinical scientific research's information management system includes:
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 distributed databases of corresponding categories;
the uniform resource scheduling module is used for uniformly scheduling the distributed file data according to clinical scientific research requirements;
the first off-line calculation module is used for carrying out off-line calculation processing on the distributed file data so as to obtain first calculation 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 performing corresponding distributed classification on the clinical medical data so as to obtain different types of clinical medical data;
the clinical data storage module is used for storing the clinical medical data of different categories;
the second off-line calculation module is used for performing off-line calculation processing on the clinical medical data so as to obtain second calculation 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 data according to the same category 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 real identity of the clinical scientific research doctor;
the authority configuration module is used for configuring corresponding operation authorities according to the identity of the clinical scientific research doctor 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 big data mining module is used for mining big data and obtaining mined medical data when relevant calculation can be carried out on the current target medical data and the mined medical data which is different from the target medical data and is beneficial to clinical scientific research is obtained after a clinical scientific research doctor obtains the corresponding target medical data;
a big data visualization module for visualizing the target medical data and the mined medical data;
and the big data guide module is used for providing an operation guide for clinical scientific research doctors.
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, an old care home medical information system, a hospital medical information system, a physical examination institution medical information system, a medical research medical information system and a medical education and training medical information system.
Preferably, the first offline calculation 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 a 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 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 a deep learning method based on feature fusion, and 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 of the sample set as a testing set;
in the training set, the medical record data, the diagnosis data, the medical advice data and the physical examination data are subjected to feature fusion and then used as the input of a deep learning model, and the treatment data and the rehabilitation data are subjected to feature fusion and then used as the output of the deep learning model, so that the deep learning model is established;
in the test set, the medical record data, the diagnosis data, the medical advice data and the physical examination data are subjected to feature fusion and then used as the input of the deep learning model, the treatment data and the rehabilitation data are subjected to feature fusion and then used as the output of the deep learning model, and a test result meets the standard to obtain a target deep learning model;
when error data are identified and deleted, the medical record data, the diagnosis data, the medical advice data and the physical examination data in the group of distributed file data to be identified are subjected to feature fusion and then serve as the input of the target deep learning model, so that standard treatment data and rehabilitation data are obtained, the standard treatment data and the rehabilitation data are compared and judged with the treatment data and the rehabilitation data in the group of distributed file data, if the standard treatment data and the rehabilitation data are not matched with the treatment data and the rehabilitation data, the group of distributed file data are identified to be error data, and the group of error data are deleted.
Preferably, a common patient identification system and a first alarm system are integrated in the clinical data acquisition module, 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 when the real-time fingerprint information is not matched with historical fingerprint information of the patient, the fingerprint identification unit sends an alarm driving instruction to the first alarm system;
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 acquiring and recognizing real-time face information of a patient, and when the real-time face information is not matched with historical face information of the patient, the face recognition unit sends an alarm driving instruction to the first alarm system;
the iris identification unit is used for acquiring and identifying real-time iris information of a patient, and when the real-time iris information is not matched with historical iris information of the patient, the iris identification unit sends an alarm driving instruction to the first alarm system;
the first alarm system is used for sending a primary alarm to clinical medical data acquisition workers.
Preferably, a standby patient identification system and a second alarm system are integrated in the clinical data acquisition module; the standby patient identity recognition system comprises a tone recognition unit, a weight recognition unit and a height recognition unit;
the tone recognition unit is used for acquiring and recognizing real-time tone information of a patient, and when the real-time tone information is not matched with historical tone information of the patient, the tone recognition unit sends an alarm driving instruction to the first alarm system;
the weight identification unit is used for acquiring and identifying real-time weight information of a patient, and when the real-time weight information is not matched with historical weight information of the patient, the weight identification unit sends an alarm driving instruction to the first alarm system;
the height identification unit is used for acquiring and identifying real-time height information of a patient, and when the real-time height information is not matched with the historical height information of the patient, the height identification unit sends an alarm driving instruction to the first alarm system;
when the second alarm system receives the alarm driving instruction of the tone recognition unit, the weight recognition unit and the height recognition unit or more than two of the tone recognition unit, the weight recognition unit and the height recognition unit, the second alarm system sends the secondary alarm to the clinical medical data acquisition staff and the guard staff secretly.
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, electrocardiogram detection data, respiration detection data, symptom detection data of the patient and self-input data of the patient.
The invention has the beneficial effects that: 1. the problems that medical data of hospitals are dispersed and independent and are difficult to integrate and process in the prior art can be solved, and the difficulty in collecting the medical data is further reduced; the medical big data are subjected to corresponding distributed classification, so that distributed file data of different categories are obtained, the target file data can be conveniently and quickly found according to requirements in the follow-up process, a large amount of sorting time and searching time can be saved, and requirements related to medical treatment are requirements for time racing, so that the parts are very important; the distributed file data are uniformly scheduled according to the clinical scientific research requirements, so that the condition of disorder in multi-party scheduling can be avoided, the whole medical big data are orderly by adopting a uniform scheduling mode, and the error utilization rate of the file data is reduced; the distributed file data is subjected to offline calculation processing, so that first calculation data which cannot be directly obtained from the distributed file data is obtained, and the comprehensiveness of the medical big data can be increased by performing offline calculation processing on the distributed file data because the distributed file data in the medical big data is incomplete, required data is not in the existing distributed file data range, and the required data can be obtained only by performing related logic calculation on the distributed file data.
2. Clinical medical data required by clinical scientific research are collected, the real-time performance of the clinical medical data can be guaranteed, 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 are classified in a corresponding distributed mode, so that different types of clinical medical data are obtained, the target clinical medical data can be conveniently searched and obtained in the follow-up process, and the follow-up processing time is saved; the clinical medical data are processed by off-line calculation, so that second calculation data which cannot be directly obtained from the clinical medical data are obtained, similarly, the directly acquired clinical medical data are not comprehensive, required partial data can be directly obtained from the clinical medical data, but required partial data cannot be obtained from the required partial data, and the required partial data can be obtained by carrying out related logic calculation, so that the second calculation data are obtained by off-line calculation on the clinical medical data, 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 are obtained, and doctors in clinical scientific research can directly obtain the cleaned and integrated target medical data and can directly use the data in clinical scientific research without performing primary processing on the data, so that the time of the doctors in clinical scientific research is saved to a certain extent.
4. After passing identity authentication, clinical scientific research doctors can search and utilize big data matched with their own authority, and can effectively mine relevant big data according to the self understanding of the searched big data, namely, relevant calculation can be carried out on the current target medical data, and when the mined medical data which is different from the target medical data and is beneficial to clinical scientific research is obtained, big data mining is carried out to obtain the mined medical data; the method provides target medical data and visual service for mining medical data for clinical scientific researchers, and provides operation guidance for the clinical scientific researchers.
Drawings
FIG. 1 is a block diagram of the architecture of one embodiment of the present invention;
FIG. 2 is a schematic diagram of a medical information system according to an embodiment of the invention;
FIG. 3 is a flow chart illustrating the steps of error data identification and deletion according to an embodiment of the present invention.
Fig. 4 is a flowchart illustrating a data correspondence determining step according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
Example (b):
aiming at the problems that in the prior art, data type companies in the medical industry mainly insist on the tradition of a hospital, the integration, interaction and fusion of data resources are realized, and from the clinical research, the sample is single; moreover, clinical data acquired by the scientific research test on the medical information system are input after various examinations are performed 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 includes:
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 distributed databases of corresponding categories;
the uniform resource scheduling module is used for uniformly scheduling the distributed file data according to clinical scientific research requirements;
the first off-line calculation module is used for carrying out off-line calculation processing on the distributed file data so as to obtain first calculation data which cannot be directly obtained from the distributed file data;
for a medical big data platform, the problems that medical data of hospitals are dispersed 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 are subjected to corresponding distributed classification, so that distributed file data of different categories are obtained, the target file data can be conveniently and quickly found according to requirements in the follow-up process, a large amount of sorting time and searching time can be saved, and requirements related to medical treatment are requirements for time racing, so that the parts are very important; the distributed file data are uniformly scheduled according to the clinical scientific research requirements, so that the condition of disorder in multi-party scheduling can be avoided, the whole medical big data are orderly by adopting a uniform scheduling mode, and the error utilization rate of the file data is reduced; the distributed file data is subjected to offline calculation processing, so that first calculation data which cannot be directly obtained from the distributed file data is obtained, and the comprehensiveness of the medical big data can be increased by performing offline calculation processing on the distributed file data because the distributed file data in the medical big data is incomplete, required data is not in the existing distributed file data range, and the required data can be obtained only by performing related logic calculation 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 performing corresponding distributed classification on the clinical medical data so as to obtain different types of clinical medical data;
the clinical data storage module is used for storing the clinical medical data of different categories;
the second off-line calculation module is used for performing off-line calculation processing on the clinical medical data so as to obtain second calculation data which cannot be directly obtained from the clinical medical data;
for a clinical data center, clinical medical data required by clinical scientific research are collected, the real-time performance of the clinical medical data can be guaranteed, 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 are classified in a corresponding distributed mode, so that different types of clinical medical data are obtained, the target clinical medical data can be conveniently searched and obtained in the follow-up process, and the follow-up processing time is saved; the clinical medical data are processed by off-line calculation, so that second calculation data which cannot be directly obtained from the clinical medical data are obtained, similarly, the directly acquired clinical medical data are not comprehensive, required partial data can be directly obtained from the clinical medical data, but required partial data cannot be obtained from the required partial data, and the required partial data can be obtained by carrying out related logic calculation, so that the second calculation data are obtained by off-line calculation on the clinical medical data, 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 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 data according to the same category to obtain target medical data required by clinical scientific research;
for a scientific research data center, cleaning distributed file data, first calculation data, clinical medical data and second calculation data, and integrating according to the same category to obtain target medical data required by clinical scientific research, wherein the target medical data obtained by doctors in clinical scientific research is cleaned and integrated directly and can be directly used for clinical scientific research without primary processing on the data, so that the time of the doctors in clinical scientific research is saved to a certain extent.
The data operation service center includes:
the doctor identity authentication module is used for authenticating the real identity of the clinical scientific research doctor;
the authority configuration module is used for configuring corresponding operation authorities according to the identity of the clinical scientific research doctor 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 big data mining module is used for mining big data and obtaining mined medical data when relevant calculation can be carried out on the current target medical data and the mined medical data which is different from the target medical data and is beneficial to clinical scientific research is obtained after a clinical scientific research doctor obtains the corresponding target medical data;
a big data visualization module for visualizing the target medical data and the mined medical data;
and the big data guide module is used for providing an operation guide for clinical scientific research doctors.
For a data operation service center, after passing identity authentication, clinical scientific research doctors can search and utilize big data matched with their own authority, and can effectively mine relevant big data according to the understanding of the searched big data, namely, when discovering that the current target medical data can also be related calculated and obtaining mined medical data which is different from the target medical data and is beneficial to clinical scientific research, mining the big data and obtaining the mined medical data; the method provides target medical data and visual service for mining medical data for clinical scientific researchers, and provides operation guidance for the clinical scientific researchers.
Aiming at the problems that in the prior art, data type companies in the medical industry mainly insist on the tradition in a hospital, the integration, interaction and fusion of data resources are realized, and from the clinical research, the sample is single. The scheme integrates, interacts and fuses all medical data in the target area.
As shown in fig. 2, preferably, the medical big data access module obtains medical big data from all medical information systems in the internet, where the medical information systems at least include a family medical information system, a community medical information system, an old care home medical information system, a hospital medical information system, a physical examination institution medical information system, a medical research medical information system, and a medical education training medical information system.
For the distributed file data in the above scheme, since the distributed file data still has errors, and the incorrect distributed file data cannot be provided to the clinical researcher, the incorrect data needs to be identified and deleted before the data is provided to the clinical researcher.
One innovative point of the present solution is that, as shown in fig. 3, preferably, the first offline calculation 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 a 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 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.
According to the scheme, the regularity of the distributed file data of the same patient is utilized, the regularity judgment of the medical record data, the diagnosis data, the medical advice data, the treatment data, the rehabilitation data and the physical examination data is provided, and the judgment accuracy is favorably provided; by adopting a single-factor judgment mode, judgment errors are easily caused, and effective and correct data loss is possibly caused.
Aiming at the regularity judgment, the scheme designs a target deep learning model attached to the scheme by using the deep learning principle to judge the correctness of data.
One innovative point of the present solution is that, as shown in fig. 4, preferably, the data correspondence is obtained by a deep learning manner 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 of the sample set as a testing set;
in the training set, the medical record data, the diagnosis data, the medical advice data and the physical examination data are subjected to feature fusion and then used as the input of a deep learning model, and the treatment data and the rehabilitation data are subjected to feature fusion and then used as the output of the deep learning model, so that the deep learning model is established;
in the test set, the medical record data, the diagnosis data, the medical advice data and the physical examination data are subjected to feature fusion and then used as the input of the deep learning model, the treatment data and the rehabilitation data are subjected to feature fusion and then used as the output of the deep learning model, and a test result meets the standard to obtain a target deep learning model;
when error data are identified and deleted, the medical record data, the diagnosis data, the medical advice data and the physical examination data in the group of distributed file data to be identified are subjected to feature fusion and then serve as the input of the target deep learning model, so that standard treatment data and rehabilitation data are obtained, the standard treatment data and the rehabilitation data are compared and judged with the treatment data and the rehabilitation data in the group of distributed file data, if the standard treatment data and the rehabilitation data are not matched with the treatment data and the rehabilitation data, the group of distributed file data are identified to be error data, and the group of error data are deleted.
In the scheme, firstly, a multi-input 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 instead of respectively inputting the plurality of input factors into the model, 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 the rehabilitation data are compared and judged with the treatment data and the rehabilitation data in the group of distributed file data, the data comparison and inspection are ensured, the error data are accurately judged, the corresponding error data are deleted, and the data accuracy is ensured.
For the clinical data acquisition module, the clinical data acquired by the patient needs to be ensured to have authenticity, so that the real identity of the patient needs to be identified and judged, and the patient is prevented from making false on the clinical data. Particularly, when a disease with a large risk is encountered, such as respiratory diseases with strong infectivity, high medical cost, and the like, the patient needs to be limited freely, some patients often have false conditions to avoid getting trouble, and then the condition has serious interest to the public.
One innovation point of the scheme is that preferably, a common patient identity recognition system and a first alarm system are integrated in the clinical data acquisition module, and 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 when the real-time fingerprint information is not matched with historical fingerprint information of the patient, the fingerprint identification unit sends an alarm driving instruction to the first alarm system;
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 acquiring and recognizing real-time face information of a patient, and when the real-time face information is not matched with historical face information of the patient, the face recognition unit sends an alarm driving instruction to the first alarm system;
the iris identification unit is used for acquiring and identifying real-time iris information of a patient, and when the real-time iris information is not matched with historical iris information of the patient, the iris identification unit sends an alarm driving instruction to the first alarm system;
the first alarm system is used for sending a primary alarm to clinical medical data acquisition workers.
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 false is very low and the cost is very high, the face information and the iris information are false in a common patient identification system for the benefit of the patient, and some scientific and technological products with extremely high cost are adopted to overcome the face identification and the iris identification, so that in the case, a standby patient identification system is designed, and the system is used for secondary identification of the patient under the condition that the patient does not know; the clinical scientific research data which can be completely ensured to be collected is the data of the patient.
One innovation point of the scheme is that preferably, a standby patient identity recognition system and a second alarm system are further integrated in the clinical data acquisition module; the standby patient identity recognition system comprises a tone recognition unit, a weight recognition unit and a height recognition unit;
the tone recognition unit is used for acquiring and recognizing real-time tone information of a patient, and when the real-time tone information is not matched with historical tone information of the patient, the tone recognition unit sends an alarm driving instruction to the first alarm system;
the weight identification unit is used for acquiring and identifying real-time weight information of a patient, and when the real-time weight information is not matched with historical weight information of the patient, the weight identification unit sends an alarm driving instruction to the first alarm system;
the height identification unit is used for acquiring and identifying real-time height information of a patient, and when the real-time height information is not matched with the historical height information of the patient, the height identification unit sends an alarm driving instruction to the first alarm system;
when the second alarm system receives the alarm driving instruction of the tone recognition unit, the weight recognition unit and the height recognition unit or more than two of the tone recognition unit, the weight recognition unit and the height recognition unit, the second alarm system sends the secondary alarm to the clinical medical data acquisition staff and the guard staff secretly.
When the second alarm system sends the second-level alarm to the clinical medical data acquisition staff secretly, it is indicated that one data is abnormal and needs to be checked by the clinical medical data acquisition staff in time, however, when the second alarm system sends the second-level alarm to the clinical medical data acquisition staff and the guard staff secretly, it is indicated that two or three data are abnormal, and the patient is in a false condition in a high probability, so that alarm information needs to be sent to the guard staff at the same time, and the false patient can be found in time by adopting an emergency response mode, so that the correctness of the clinical scientific research data is ensured, and even the benefit of the public can be ensured.
Aiming at the patients who are not convenient to go out, the patients who need to be at home to carry out clinical data acquisition, such as the period of the epidemic situation of the large-scale infectious disease, a mobile clinical data acquisition module is provided, and the acquired clinical medical data can be transmitted to a 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, electrocardiogram detection data, respiration detection data, symptom detection data of the patient and self-input data of the patient.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. Utilize medical treatment big data to support information management system of clinical scientific research, its characterized in that includes:
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 distributed databases of corresponding categories;
the uniform resource scheduling module is used for uniformly scheduling the distributed file data according to clinical scientific research requirements;
the first off-line calculation module is used for carrying out off-line calculation processing on the distributed file data so as to obtain first calculation 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 performing corresponding distributed classification on the clinical medical data so as to obtain different types of clinical medical data;
the clinical data storage module is used for storing the clinical medical data of different categories;
the second off-line calculation module is used for performing off-line calculation processing on the clinical medical data so as to obtain second calculation 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 data according to the same category 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 real identity of the clinical scientific research doctor;
the authority configuration module is used for configuring corresponding operation authorities according to the identity of the clinical scientific research doctor 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 big data mining module is used for mining big data and obtaining mined medical data when relevant calculation can be carried out on the current target medical data and the mined medical data which is different from the target medical data and is beneficial to clinical scientific research is obtained after a clinical scientific research doctor obtains the corresponding target medical data;
a big data visualization module for visualizing the target medical data and the mined medical data;
and the big data guide module is used for providing an operation guide for clinical scientific research doctors.
2. The information management system for supporting clinical research by using medical big data as claimed in claim 1, wherein the medical big data access module obtains the medical big data from all medical information systems in the internet, wherein the medical information systems at least include a family medical information system, a community medical information system, an old care home medical information system, a hospital medical information system, a physical examination institution medical information system, a medical research medical information system, and a medical education training medical information system.
3. The information management system for supporting clinical research with medical big data according to claim 1, wherein the first offline calculation 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 a 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 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 research with medical big data according to claim 3, wherein the data correspondence is obtained by a deep learning method based on feature fusion, and specifically comprises:
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 of the sample set as a testing set;
in the training set, the medical record data, the diagnosis data, the medical advice data and the physical examination data are subjected to feature fusion and then used as the input of a deep learning model, and the treatment data and the rehabilitation data are subjected to feature fusion and then used as the output of the deep learning model, so that the deep learning model is established;
in the test set, the medical record data, the diagnosis data, the medical advice data and the physical examination data are subjected to feature fusion and then used as the input of the deep learning model, the treatment data and the rehabilitation data are subjected to feature fusion and then used as the output of the deep learning model, and a test result meets the standard to obtain a target deep learning model;
when error data are identified and deleted, the medical record data, the diagnosis data, the medical advice data and the physical examination data in the group of distributed file data to be identified are subjected to feature fusion and then serve as the input of the target deep learning model, so that standard treatment data and rehabilitation data are obtained, the standard treatment data and the rehabilitation data are compared and judged with the treatment data and the rehabilitation data in the group of distributed file data, if the standard treatment data and the rehabilitation data are not matched with the treatment data and the rehabilitation data, the group of distributed file data are identified to be error data, and the group of error data are deleted.
5. The information management system for supporting clinical research by using medical big data as claimed in claim 1, wherein a common patient identification system and a first alarm system are integrated in the clinical data acquisition module, 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 when the real-time fingerprint information is not matched with historical fingerprint information of the patient, the fingerprint identification unit sends an alarm driving instruction to the first alarm system;
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 acquiring and recognizing real-time face information of a patient, and when the real-time face information is not matched with historical face information of the patient, the face recognition unit sends an alarm driving instruction to the first alarm system;
the iris identification unit is used for acquiring and identifying real-time iris information of a patient, and when the real-time iris information is not matched with historical iris information of the patient, the iris identification unit sends an alarm driving instruction to the first alarm system;
the first alarm system is used for sending a primary alarm to clinical medical data acquisition workers.
6. The information management system for supporting clinical research with medical big data as claimed in claim 5, wherein a standby patient identification system and a second alarm system are further integrated inside the clinical data acquisition module; the standby patient identity recognition system comprises a tone recognition unit, a weight recognition unit and a height recognition unit;
the tone recognition unit is used for acquiring and recognizing real-time tone information of a patient, and when the real-time tone information is not matched with historical tone information of the patient, the tone recognition unit sends an alarm driving instruction to the first alarm system;
the weight identification unit is used for acquiring and identifying real-time weight information of a patient, and when the real-time weight information is not matched with historical weight information of the patient, the weight identification unit sends an alarm driving instruction to the first alarm system;
the height identification unit is used for acquiring and identifying real-time height information of a patient, and when the real-time height information is not matched with the historical height information of the patient, the height identification unit sends an alarm driving instruction to the first alarm system;
when the second alarm system receives the alarm driving instruction of the tone recognition unit, the weight recognition unit and the height recognition unit or more than two of the tone recognition unit, the weight recognition unit and the height recognition unit, the second alarm system sends the secondary alarm to the clinical medical data acquisition staff and the guard staff secretly.
7. The information management system for supporting clinical research with medical big data as claimed in claim 6, wherein the clinical data collection module further comprises a mobile clinical data collection module disposed at the patient's residence, the mobile clinical data collection module is used for collecting clinical medical data of the patient that is not convenient to move.
8. The information management system for supporting clinical research with medical big data according to claim 7, wherein the mobile clinical data collection module is used for collecting electroencephalogram detection data, heart rate detection data, blood detection data, pulse detection data, body temperature detection data, electrocardio detection data, respiration detection data, symptom detection data and patient self-input data of a patient.
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