CN113808731A - Intelligent medical diagnosis system and method - Google Patents
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Abstract
The present application provides an intelligent medical diagnostic system and method, the system comprising: the system comprises an acquisition module, a retrieval module and a decision-making module, wherein the acquisition module is used for acquiring clinical data input by a user, and the clinical data comprises: clinical diagnostic data and clinical prescription data; the retrieval module is used for determining the similarity between the clinical data and each corresponding historical clinical data in the preset knowledge database according to the clinical data; the decision module is used for obtaining a preset number of decision data corresponding to the historical clinical data according to the sorting results of the similarity from large to small, and determining the decision results according to the decision data. The method provides doctors with less clinical experience with very targeted clinical knowledge, and effectively improves the diagnosis efficiency of the doctors and the effectiveness of treatment schemes.
Description
Technical Field
The invention relates to the technical field of medical diagnosis, in particular to an intelligent medical diagnosis system and method.
Background
At present, with the rapid development of socio-economy and the change of life concept, the life style and behavior of most people are changed, resulting in the continuous increase of the prevalence rate of various diseases.
In the prior art, when a doctor makes a diagnosis for a patient, the doctor needs to determine a treatment scheme according to the symptoms of the patient.
However, since the clinical experience of doctors is uneven, and the treatment plan determined by different doctors is usually different for patients with the same disease, there is a certain difference in treatment effect, so that the diagnosis efficiency and the effectiveness of the treatment plan are relatively low for doctors with less clinical experience, and the diagnosis efficiency is affected even for doctors with more experience when facing a large number of patients. Therefore, a tool for assisting a doctor in medical diagnosis is urgently needed, and the tool has great significance for improving the diagnosis efficiency of the doctor.
Disclosure of Invention
Therefore, the technical problem to be solved by the present invention is to overcome the defects of low diagnosis efficiency and low effectiveness of treatment schemes of some doctors in the prior art, and to provide an intelligent medical diagnosis system and method.
A first aspect of the present application provides a square intelligent medical diagnostic system, comprising: an acquisition module, a retrieval module and a decision module, wherein,
the acquisition module is used for acquiring clinical data input by a user, and the clinical data comprises: clinical diagnostic data and clinical prescription data;
the retrieval module is used for determining the similarity between the clinical data and each corresponding historical clinical data in a preset knowledge database according to the clinical data;
the decision module is used for acquiring a preset number of decision data corresponding to the historical clinical data according to the sorting result of the similarity from large to small, and determining a decision result according to each decision data.
Optionally, when the clinical data is clinical condition data, the decision module is specifically configured to: and determining a diagnosis result and a treatment scheme corresponding to the clinical disease data according to each decision data.
Optionally, when the clinical data is clinical prescription data, the decision module is specifically configured to:
determining difference data between the clinical prescription data and each historical prescription data;
optimizing the clinical prescription data based on the difference data.
Optionally, the decision module is further configured to:
determining treatment efficacy, treatment symptoms and methods of use of the historical prescription data based on the constituent elements of the historical prescription data having the highest similarity to the clinical prescription data.
Optionally, the system further includes: a display module;
the display module is used for displaying the decision result.
Optionally, when the clinical data is clinical condition data,
the retrieval module is further used for acquiring guide information and/or literature information corresponding to the clinical disease data from the preset database and pushing the guide information and/or literature information to the display module for display.
Optionally, the system further includes: a treatment technique decision module;
and the treatment technology decision module is used for determining the optimal treatment technology according to the decision result and pushing the optimal treatment technology to the display module for display.
Optionally, the system further comprises a health preserving pushing module;
and the health promotion pushing module is used for determining an optimal health promotion scheme according to the decision result and pushing the optimal health promotion scheme to the display module for display.
In a second aspect, the present application provides an intelligent medical diagnosis method, including:
acquiring clinical data, the clinical data comprising: clinical diagnostic data and clinical prescription data;
according to the clinical data, determining the similarity between the clinical data and each historical clinical data in a preset knowledge database;
and obtaining a preset number of decision data corresponding to the historical clinical data according to the sequencing results of the similarity from large to small, and determining a decision result according to each decision data.
Optionally, when the clinical data is clinical condition data, the decision module is specifically configured to: and determining a diagnosis result and a treatment scheme corresponding to the clinical disease data according to each decision data.
Optionally, when the clinical data is clinical prescription data, determining difference data between the clinical prescription data and each historical prescription data;
optimizing the clinical prescription data based on the difference data.
Optionally, the method further includes:
determining treatment efficacy, treatment symptoms and methods of use of the historical prescription data based on the constituent elements of the historical prescription data having the highest similarity to the clinical prescription data.
Optionally, the method further includes: and displaying the decision result.
Optionally, when the clinical data is clinical condition data, the method further comprises:
and acquiring guide information and/or literature information corresponding to the clinical disease data from the preset database, and pushing the guide information and/or literature information to be displayed.
Optionally, the method further includes:
and determining the optimal treatment technology according to the decision result, and displaying the optimal treatment technology.
Optionally, the method further comprises;
and determining an optimal health preserving scheme according to the decision result, and displaying the optimal health preserving scheme.
A third aspect of the present application provides an electronic device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the method as set forth in the second aspect above and in various possible designs of the second aspect.
A fourth aspect of the present application provides a computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, perform the method as set forth in the second aspect above and in various possible designs of the second aspect.
The intelligent medical diagnosis system and method provided by the application comprise an acquisition module, a retrieval module and a decision-making module, wherein the acquisition module is used for acquiring clinical data input by a user, and the clinical data comprise: clinical diagnostic data and clinical prescription data; the retrieval module is used for determining the similarity between the clinical data and each corresponding historical clinical data in the preset knowledge database according to the clinical data; the decision module is used for obtaining a preset number of decision data corresponding to the historical clinical data according to the sorting results of the similarity from large to small, and determining the decision results according to the decision data. According to the system provided by the scheme, the historical clinical data which are similar to the clinical data are determined by calculating the similarity of the clinical data and the historical clinical data in the preset knowledge database, and then the diagnosis result and the treatment scheme which correspond to the historical clinical data are obtained, so that the system provides targeted clinical knowledge for doctors with less clinical experience, and effectively improves the diagnosis efficiency and the effectiveness of the treatment scheme of the doctors.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic structural diagram of an intelligent medical diagnosis system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of another intelligent medical diagnostic system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an exemplary display module according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of another intelligent medical diagnosis system provided in an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of another intelligent medical diagnosis system provided in an embodiment of the present invention;
FIG. 6 is a schematic diagram of a network architecture on which an exemplary intelligent medical diagnosis system is based according to an embodiment of the present invention;
FIG. 7 is a schematic flow chart of an intelligent medical diagnosis method provided by an embodiment of the invention;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The intelligent medical diagnosis system and method provided by the embodiment of the application are suitable for assisting doctors in performing medical diagnosis so as to improve the diagnosis efficiency of the doctors and the effectiveness of treatment schemes. In the prior art, when a doctor makes a diagnosis for a patient, the doctor needs to determine a treatment scheme according to the symptoms of the patient. However, since the clinical experience of doctors is uneven, and the treatment plan determined by different doctors is usually different for patients with the same disease, there is a certain difference in treatment effect, so that the diagnosis efficiency and the effectiveness of the treatment plan are relatively low for doctors with less clinical experience, and the diagnosis efficiency is affected even for doctors with more experience when facing a large number of patients. Therefore, a tool for assisting a doctor in medical diagnosis is urgently needed, and the tool has great significance for improving the diagnosis efficiency of the doctor.
In view of the above problems, an intelligent medical diagnosis system provided in an embodiment of the present application includes an obtaining module, a retrieving module, and a decision module, where the obtaining module is configured to obtain clinical data input by a user, and the clinical data includes: clinical diagnostic data and clinical prescription data; the retrieval module is used for determining the similarity between the clinical data and each corresponding historical clinical data in the preset knowledge database according to the clinical data; the decision module is used for obtaining a preset number of decision data corresponding to the historical clinical data according to the sorting results of the similarity from large to small, and determining the decision results according to the decision data. According to the system provided by the scheme, the historical clinical data which are similar to the clinical data are determined by calculating the similarity of the clinical data and the historical clinical data in the preset knowledge database, and then the diagnosis result and the treatment scheme which correspond to the historical clinical data are obtained, so that the system provides targeted clinical knowledge for doctors with less clinical experience, and effectively improves the diagnosis efficiency and the effectiveness of the treatment scheme of the doctors.
The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
The embodiment of the application provides an intelligent medical diagnosis system, which is used for overcoming the defects that the diagnosis efficiency of part of doctors and the effectiveness of treatment schemes in the prior art are low.
As shown in fig. 1, a schematic structural diagram of an intelligent medical diagnosis system provided in an embodiment of the present application is shown, where the intelligent medical diagnosis system 10 includes: an obtaining module 101, a retrieving module 102 and a decision module 103.
The obtaining module 101 is configured to obtain clinical data input by a user, where the clinical data includes: clinical diagnostic data and clinical prescription data; the retrieval module 102 is configured to determine, according to the clinical data, a similarity between the clinical data and each corresponding historical clinical data in the preset knowledge database; the decision module 103 is configured to obtain a preset number of decision data corresponding to the historical clinical data according to the sorting result of the similarity from large to small, and determine a decision result according to each decision data.
It should be noted that the clinical diagnosis data is determined by the user according to the current condition of the patient, the clinical prescription data is determined by the user according to the current condition of the patient, and the historical clinical data includes official medical guidelines, similar cases, famous experience, and the like, wherein the user may refer to a doctor or other medical staff.
Specifically, in an embodiment, a medical diagnosis model may be established based on a convolutional neural network, the model is optimally trained, the optimized medical diagnosis model may identify clinical data input by a user, a medical scenario of the clinical data is simulated, and finally, a decision result corresponding to historical clinical data that is similar to the medical scenario of the clinical data is pushed to the user based on an Agent technology.
Specifically, in one embodiment, the retrieval module 102 may perform data feature extraction on clinical data input by a user based on natural language processing technology and perform vectorization processing on the data features to generate corresponding clinical data feature vectors. Similarly, a historical clinical data feature vector corresponding to each historical clinical data in the preset knowledge database may be generated, and finally, the similarity between the clinical data and each historical clinical data may be obtained by performing similarity calculation on the clinical data feature vector and each historical clinical data feature. The embodiment of the present application is not limited to the specific manner of calculating the similarity.
Specifically, in one embodiment, when the clinical data is clinical condition data, the decision module 103 is specifically configured to: and determining a diagnosis result and a treatment scheme corresponding to the clinical disease data according to the decision data.
The decision data may include, among other things, diagnostic results and treatment regimens in various historical clinical data similar to the clinical data.
In order to improve the diagnosis efficiency of the user, the decision module 103 may further directly obtain the diagnosis results and the treatment schemes in the preset number of decision results according to the sorting results of the similarity corresponding to the clinical data input by the user.
Illustratively, when the system is applied to the traditional Chinese medical diagnosis, the clinical disease data mainly comprises the specific diseases, the diagnosis result mainly comprises the disease names, and the treatment scheme mainly comprises a main prescription, a prescription composition, a Chinese patent medicine and related treatment technologies. The system can define the attributes of the disease by taking the disease as the center according to the input traditional Chinese medicine disease name, provides the famous medical experience, diagnosis and treatment specifications, typical cases, famous medical famous prescriptions, clinical evidence based on the traditional Chinese medicine and western medicine disease diagnosis, and the like, and can learn the academic idea and clinical syndrome differentiation experience of famous medical doctors aiming at a certain disease or syndrome type while determining the diagnosis result and the treatment scheme based on the intelligent medical diagnosis system.
Accordingly, when the clinical data is clinical prescription data, the decision module 103 is specifically configured to: determining difference data between the clinical prescription data and each historical prescription data; clinical prescription data is optimized based on the difference data.
Illustratively, if the clinical prescription data includes licorice, angelica, bitter orange and white peony root, and the historical prescription data with the highest similarity includes codonopsis pilosula, gardenia, licorice, angelica, madder, astragalus, bupleurum, bitter orange, cacumen biotae, atractylodes, moutan bark, motherwort, white peony root and cattail pollen, the difference data between the clinical prescription data and the historical prescription data is determined to include codonopsis pilosula, gardenia, madder, astragalus, bupleurum, cacumen biotae, atractylodes, moutan bark, motherwort and cattail pollen, and the decision module 103 supplements and adjusts the clinical prescription data according to the difference data to improve the medical effect of the clinical prescription data. Wherein, while optimizing the clinical prescription data, the Chinese patent medicine corresponding to the components and the curative effect of the Chinese patent medicine, such as compound motherwort oral liquid, can also be determined according to the optimized clinical prescription data.
To further improve the medical outcome of the clinical prescription data, in one embodiment, the decision module 103 is further configured to: and determining the treatment efficacy, the main treatment symptoms and the using method of the historical prescription data according to the component elements of the historical prescription data with the highest similarity to the clinical prescription data.
Specifically, when the user applies the system, the optimized clinical prescription data can be further adjusted according to the treatment effect, the indication disease and the use method of the historical prescription data and the actual situation of the clinical patient, so that the adjusted treatment scheme is more targeted, and the medical effect of the clinical prescription data is further improved. The treatment scheme adjusted by the user is automatically stored in a preset knowledge database of the system so as to provide other users with relevant medical knowledge.
On the basis of the above embodiments, in order to improve the utilization efficiency of the system, fig. 2 is a schematic structural diagram of another intelligent medical diagnosis system provided in the embodiments of the present application, and as an implementable manner, on the basis of the above embodiments, in an embodiment, the system further includes a display module 104.
The display module 104 is used for displaying the decision result.
Specifically, the display module 104 can perform visualization processing on the decision result to display the decision result more intuitively and clearly, so that the user can quickly obtain the decision result, thereby improving the use efficiency of the system.
Specifically, when the clinical data is clinical condition data, the retrieval module 102 is further configured to obtain guide information and/or literature information corresponding to the clinical condition data from a preset database, and push the guide information and/or literature information to the display module 104 for display.
Fig. 3 is a schematic structural diagram of an exemplary display module 104 according to an embodiment of the present disclosure. The display module 104 shown in fig. 3 may include clinical information and decision reference for decision results, the clinical disease data mainly includes clinical symptoms, the diagnosis results mainly include disease names, the treatment plan mainly includes law-law therapy, principal formula, prescription composition, Chinese patent medicine and related treatment technologies, and the decision reference is a main literature source of the decision results.
On the basis of the above embodiments, in order to further improve the diagnosis efficiency of the doctor and the effectiveness of the treatment scheme, fig. 4 is a schematic structural diagram of another intelligent medical diagnosis system provided in the embodiments of the present application, and as an implementable manner, on the basis of the above embodiments, the system further includes a treatment technology decision module 105.
The treatment technology decision module 105 is configured to determine an optimal treatment technology according to the decision result, and push the optimal treatment technology to the display module 104 for display.
Illustratively, when the system is applied to the diagnosis of the traditional Chinese medicine, the treatment scheme of the traditional Chinese medicine comprises a main prescription, a prescription composition and a traditional Chinese medicine, and also comprises treatment technologies, wherein the treatment technologies can comprise acupuncture, scraping and the like. And the treatment technology decision module determines the corresponding treatment technology, namely the optimal treatment technology according to the decision result.
Specifically, taking the example that the treatment technique is acupuncture, the optimal treatment technique may include needles for acupuncture and acupuncture points corresponding to the respective needles.
On the basis of the above embodiment, in order to further improve the diagnosis efficiency of the doctor and the effectiveness of the treatment scheme, fig. 5 is a schematic structural diagram of another intelligent medical diagnosis system provided in the embodiment of the present application, and as an implementable manner, on the basis of the above embodiment, the system further includes a health promotion pushing module 106.
The health preserving pushing module 106 is configured to determine an optimal health preserving scheme according to the decision result, and push the optimal health preserving scheme to the display module for display.
Specifically, the regimen may include a diet regimen and an exercise plan, so that the doctor may provide a better auxiliary treatment plan for the patient according to the determined regimen to improve the cure rate of the patient.
In an embodiment, the intelligent medical diagnosis system provided by the embodiment of the application can be embedded into an electronic medical record system of a hospital, and the intelligent medical diagnosis system can analyze the medication experience of a doctor and simultaneously perform disease correlation analysis according to a plurality of patient medical records stored in the electronic medical record system by the doctor, read the decision history of the doctor, and push the determined related information to a display module for display. In order to improve readability of the related information, the medication experience analysis result may be displayed based on a histogram, and the disease correlation analysis result may be displayed based on a sector graph, and a specific display manner is not limited in the embodiment of the present application.
As an implementable manner, as shown in fig. 6, a schematic diagram of a network structure on which the exemplary intelligent medical diagnosis system provided in the embodiment of the present application is based is provided.
The intelligent medical diagnosis system provided in the embodiment of the present application may be established based on contextual models, so each contextual logic needs to establish an ontology database, and each ontology database may have a plurality of triple relations, for example, 3 triple relations may be established, which are "disease-relation-syndrome", "syndrome-chief complaint relation-symptom", "syndrome-assistant complaint relation-symptom", respectively. Triplets in ontological libraries for different scenarios can be multiplexed
As an implementable mode, the intelligent medical diagnosis system can be embedded into an electronic medical record system as an intelligent knowledge engine to run in real time, relevant patient disease information is collected, and when a doctor inputs relevant diagnosis and treatment information of a patient, the system is triggered to provide an auxiliary decision. The system operation process comprises the following steps: scene capture judgment, data confirmation, data collection, data classification, knowledge supply, decision result display and decision process termination. For different working situations of doctors in the electronic medical records, the system can automatically identify and call services and actively make decision support. The scene analysis comprises the following steps: diseases, syndromes, therapeutic methods, prescriptions, additions, subtractions, and other processes. The system decomposes the whole operation function into independent function operation units, including a format conversion Agent, a classification identification Agent, a scene judgment Agent, a rule matching Agent, a knowledge base Agent (group) and a public knowledge Agent, and provides basic data, configuration information and database interface calling. The system mainly realizes the functions of supporting system operation, intelligent decision and system self-learning. The system interface comprises a user interface, an external interface and an internal interface. And the system provides a display window, the decision result is displayed in a fixed window form in a data block list mode, and a user can select to click a data block link, call the result and load the result into the EMR system. External interface: EMR data is acquired. When the cursor falls in the corresponding EMR system control, the Agent judges the scene requirement and captures various types of data from the EMR. And on an electronic medical record text entry interface, the words are scribed through a mouse, a right key is clicked, and an intelligent clinical decision is selected. Internal interface: including calling mode between modules, feedback response, input and output of interface, etc.
The intelligent medical diagnosis system provided by the embodiment of the application comprises an acquisition module, a retrieval module and a decision module, wherein the acquisition module is used for acquiring clinical data input by a user, and the clinical data comprise: clinical diagnostic data and clinical prescription data; the retrieval module is used for determining the similarity between the clinical data and each corresponding historical clinical data in the preset knowledge database according to the clinical data; the decision module is used for obtaining a preset number of decision data corresponding to the historical clinical data according to the sorting results of the similarity from large to small, and determining the decision results according to the decision data. According to the system provided by the scheme, the historical clinical data which are similar to the clinical data are determined by calculating the similarity of the clinical data and the historical clinical data in the preset knowledge database, and then the diagnosis result and the treatment scheme which correspond to the historical clinical data are obtained, so that the system provides targeted clinical knowledge for doctors with less clinical experience, and effectively improves the diagnosis efficiency and the effectiveness of the treatment scheme of the doctors.
The embodiment of the present application further provides an intelligent medical diagnosis method, which is used for overcoming the defects that the diagnosis efficiency and the effectiveness of a treatment scheme of some doctors in the prior art are low.
As shown in fig. 7, a schematic flow chart of an intelligent medical diagnosis method provided in an embodiment of the present application is shown, where the method includes:
and 703, acquiring a preset number of decision data corresponding to the historical clinical data according to the sorting results of the similarity from large to small, and determining a decision result according to each decision data.
Specifically, in one embodiment, when the clinical data is clinical condition data, the diagnosis result and the treatment plan corresponding to the clinical condition data are determined according to each decision data.
Specifically, in one embodiment, when the clinical data is clinical prescription data and the decision data is historical prescription data, determining difference data between the clinical prescription data and the historical prescription data; clinical prescription data is optimized based on the difference data.
Specifically, in an embodiment, the intelligent medical diagnosis method provided in the embodiment of the present application further includes: and determining the treatment efficacy, the main treatment symptoms and the using method of the historical prescription data according to the component elements of the historical prescription data with the highest similarity to the clinical prescription data.
Specifically, in an embodiment, the intelligent medical diagnosis method provided in the embodiment of the present application further includes: and displaying the decision result.
Specifically, in one embodiment, when the clinical data is clinical condition data, the method further comprises: and acquiring guide information and/or literature information corresponding to the clinical disease data from a preset database, and pushing the guide information and/or the literature information to be displayed.
Specifically, in an embodiment, the intelligent medical diagnosis method provided in the embodiment of the present application further includes: and determining the optimal treatment technology according to the decision result, and displaying the optimal treatment technology.
Specifically, in an embodiment, the intelligent medical diagnosis method provided by the embodiment of the present application further includes; and determining an optimal health preserving scheme according to the decision result, and displaying the optimal health preserving scheme.
The intelligent medical diagnosis method provided by the embodiment of the application is a specific processing procedure of the intelligent medical diagnosis system provided by the embodiment, and the implementation manner and the principle are the same, and are not repeated.
The embodiment of the application also provides electronic equipment which is used for executing the method provided by the embodiment.
Fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device 80 includes: at least one processor 81 and memory 82;
wherein execution of the memory-stored computer-executable instructions by the at least one processor causes the at least one processor to perform the instructions of the method as in any one of the preceding embodiments.
The electronic device provided by the embodiment of the application is used for executing the intelligent medical diagnosis method provided by the embodiment, and the implementation manner and the principle of the electronic device are the same, so that the detailed description is omitted.
The embodiment of the present application provides a storage medium containing computer executable instructions, where the storage medium stores computer processor execution instructions, and when the processor executes the computer execution instructions, the method provided in any one of the above embodiments is implemented.
The storage medium containing the computer-executable instructions according to the embodiment of the present application may be used to store the computer-executable instructions of the intelligent medical diagnosis method provided in the foregoing embodiment, and the implementation manner and the principle thereof are the same and are not described again.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (10)
1. An intelligent medical diagnostic system, comprising: an acquisition module, a retrieval module and a decision module, wherein,
the acquisition module is used for acquiring clinical data input by a user, and the clinical data comprises: clinical diagnostic data and clinical prescription data;
the retrieval module is used for determining the similarity between the clinical data and each corresponding historical clinical data in a preset knowledge database according to the clinical data;
the decision module is used for acquiring a preset number of decision data corresponding to the historical clinical data according to the sorting result of the similarity from large to small, and determining a decision result according to each decision data.
2. The system of claim 1, wherein when the clinical data is clinical condition data, the decision module is specifically configured to: and determining a diagnosis result and a treatment scheme corresponding to the clinical disease data according to each decision data.
3. The system of claim 1, wherein when the clinical data is clinical prescription data, the decision module is specifically configured to:
determining difference data between the clinical prescription data and each historical prescription data;
optimizing the clinical prescription data based on the difference data.
4. The system of claim 3, wherein the decision module is further configured to:
determining treatment efficacy, treatment symptoms and methods of use of the historical prescription data based on the constituent elements of the historical prescription data having the highest similarity to the clinical prescription data.
5. The system of claim 1, further comprising: a display module;
the display module is used for displaying the decision result.
6. The system of claim 5, wherein when the clinical data is clinical condition data,
the retrieval module is further used for acquiring guide information and/or literature information corresponding to the clinical disease data from the preset database and pushing the guide information and/or literature information to the display module for display.
7. The system of claim 5, further comprising: a treatment technology decision module and a health preserving push module;
the treatment technology decision module is used for determining the optimal treatment technology according to the decision result and pushing the optimal treatment technology to the display module for display;
and the health promotion pushing module is used for determining an optimal health promotion scheme according to the decision result and pushing the optimal health promotion scheme to the display module for display.
8. An intelligent medical diagnostic method, characterized in that the method comprises:
acquiring clinical data, the clinical data comprising: clinical diagnostic data and clinical prescription data;
according to the clinical data, determining the similarity between the clinical data and each historical clinical data in a preset knowledge database;
and obtaining a preset number of decision data corresponding to the historical clinical data according to the sequencing results of the similarity from large to small, and determining a decision result according to each decision data.
9. An electronic device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the method of claim 8.
10. A storage medium containing computer-executable instructions for performing the method of claim 8 when executed by a computer processor.
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