WO2020037454A1 - Système et procédé de diagnostic et de traitement auxiliaires intelligents - Google Patents

Système et procédé de diagnostic et de traitement auxiliaires intelligents Download PDF

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Publication number
WO2020037454A1
WO2020037454A1 PCT/CN2018/101271 CN2018101271W WO2020037454A1 WO 2020037454 A1 WO2020037454 A1 WO 2020037454A1 CN 2018101271 W CN2018101271 W CN 2018101271W WO 2020037454 A1 WO2020037454 A1 WO 2020037454A1
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Prior art keywords
disease
treatment
diagnosis
medical records
medical record
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PCT/CN2018/101271
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English (en)
Chinese (zh)
Inventor
张�雄
韩恩莉
舒振峰
石宇
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深圳市全息医疗科技有限公司
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Application filed by 深圳市全息医疗科技有限公司 filed Critical 深圳市全息医疗科技有限公司
Priority to PCT/CN2018/101271 priority Critical patent/WO2020037454A1/fr
Priority to CN201880008002.1A priority patent/CN110249392A/zh
Publication of WO2020037454A1 publication Critical patent/WO2020037454A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • the present invention relates to the field of medical information technology, and in particular, to an intelligent auxiliary diagnosis and treatment system and method.
  • the object of the present invention is to provide an intelligent auxiliary diagnosis and treatment system and method to realize accurate diagnosis and treatment of patients, and greatly improve the diagnosis and treatment level and work efficiency of the hospital.
  • an embodiment of the present invention provides an intelligent assisted diagnosis and treatment system, which is characterized by including:
  • a classification module configured to classify the received medical records according to the type of the disease, and store the classified medical records into a corresponding database
  • a matching module configured to match the medical record with a medical record template in a medical record template library to obtain a medical record template with a matching degree greater than a preset threshold, wherein the medical record template stores a disease type and disease information corresponding to the disease type;
  • Auxiliary diagnosis module used to obtain the matched disease type and the disease information corresponding to the disease type, use the artificial intelligence convolutional neural network learning method to give the corresponding historical diagnosis reference scheme, and output the matched disease type and the disease type Corresponding illness information for doctor's reference.
  • it further comprises:
  • a conversion module is used to convert text medical records into standardized electronic medical records through text and image analysis in accordance with artificial intelligence deep learning methods, and convert non-standardized electronic medical records and semi-normalized electronic medical records into standardized electronic medical records according to system preset rules.
  • it further comprises:
  • the artificial intelligence module is used to comprehensively compare the treatment plans of the matched disease types by the plurality of doctors according to the patient's disease type, and obtain the diagnosis and treatment plan including the corresponding disease information and similar disease information to the auxiliary diagnosis module.
  • it further comprises:
  • the tracking and monitoring module is used to check whether there are unchecked items or undiagnosed processes in the medical record after the doctor determines the type of the patient's disease, and timely feedback the implementation progress of the treatment plan and give treatment in the subsequent treatment plan
  • the effect analysis report indicates whether the doctor needs to modify the treatment plan for secondary auxiliary diagnosis and treatment during the treatment process.
  • it further comprises:
  • the remote communication module is used to provide an external interface for the communication between the intelligent auxiliary diagnosis and treatment system and the remote auxiliary system, and to receive a remote access request from a doctor, and to establish communication with the remote assist system according to the access request for remote online assistance.
  • it further comprises:
  • a voice recognition module is used to collect and recognize a voice signal, convert the recognized voice information into text information, and record the corresponding voice information in an outpatient medical record template.
  • the present invention also provides an intelligent assisted diagnosis and treatment method, which includes the following steps:
  • Obtain the matched disease type and the disease information corresponding to the disease type use the artificial intelligence convolutional neural network learning method to give the corresponding historical diagnosis reference scheme, and output the matched disease type and the disease information corresponding to the disease type, to For your doctor's reference.
  • the medical records include electronic medical records and / or file medical records, and after receiving the medical records, they further include:
  • the text medical records are converted into standardized electronic medical records through text and image analysis according to artificial intelligence deep learning methods;
  • the received medical records are non-standardized electronic medical records and semi-standardized electronic medical records
  • the non-standardized electronic medical records and semi-standardized electronic medical records are converted into standardized electronic medical records according to the preset rules of the system.
  • the condition information for your doctor's reference is followed by:
  • a plurality of doctors comprehensively compare the treatment plans of the matched disease types, and obtain a diagnosis and treatment plan including the corresponding disease information and similar disease information and transmit it to the doctor.
  • it further comprises:
  • it further comprises:
  • it further comprises:
  • an outpatient medical record template receive the doctor's voice signal, recognize the collected voice signal, convert the recognized voice information into text information, and record it into the corresponding position in the outpatient medical record template to generate a medical record.
  • the intelligent auxiliary diagnosis and treatment system and method provided by the present invention match electronic medical records with the medical record templates of the historical medical record template library, and then use artificial intelligence to learn the experience of each doctor and expert treatment plan.
  • the diagnosis and treatment methods for intelligent diagnosis and treatment of diseases, the combination of the most effective treatment methods for historically related diseases and the current treatment experience of each such expert are given to assist ordinary doctors in diagnosis. Therefore, the present invention solves the problem of insufficient doctor level in current medical and health services, which not only can improve the doctor's medical level, but also effectively reduce the doctor's misdiagnosis rate.
  • FIG. 1 is a schematic diagram of interaction with a hospital terminal when the intelligent auxiliary diagnosis and treatment system of the present invention is applied to a hospital server;
  • FIG. 2 is a schematic diagram of a hardware architecture of a server applied to the intelligent auxiliary diagnosis and treatment system of the present invention
  • FIG. 3 is a functional module schematic diagram of a first embodiment of an intelligent assisted diagnosis and treatment system provided by the present invention
  • FIG. 4 is a schematic diagram of a functional module of a second embodiment of the intelligent auxiliary diagnosis and treatment system provided by the present invention.
  • FIG. 5 is a schematic diagram of a login interface of the intelligent assisted diagnosis and treatment system provided by the present invention.
  • FIG. 6 is a schematic diagram of an interface of the intelligent assisted diagnosis and treatment system provided by the present invention after login;
  • FIG. 7 is a schematic diagram of functional modules of a third embodiment of the intelligent auxiliary diagnosis and treatment system provided by the present invention.
  • FIG. 8 is a flowchart of a first embodiment of a method for intelligently assisted diagnosis and treatment provided by the present invention.
  • FIG. 9 is a flowchart of a second embodiment of a method for intelligently assisted diagnosis and treatment provided by the present invention.
  • FIG. 10 is a flowchart of a third embodiment of the intelligent assisted diagnosis and treatment method provided by the present invention.
  • the intelligent auxiliary diagnosis and treatment system of the present invention may be set in a server 100.
  • the server 100 can be accessed by in-hospital terminals 500 (for example, computer terminals of various hospitals, diagnosis and treatment terminals, etc.) and other hospital terminals 300 linked through the cloud platform 200.
  • Various types of hospitals can communicate with the server 100 through the network cloud platform 200. That is, the server 100 may be located in a local area network of a hospital, and may be accessed by other hospitals affiliated to the hospital through a wide area network or mobile Internet.
  • valid authentication must be performed, which includes doctor's facial recognition, identity information password login or voice recognition. Only the authenticated user or terminal can access the database 400.
  • the server 100 also has a database access function.
  • the database 400 is used to store various types of data, such as medical knowledge, patient diagnosis information, doctors' diagnosis experience, and treatment plan.
  • the server 100 receives the processing requirements of the user or the terminal, and wants to obtain more comparison of the disease information according to the terminal requirements, it can also access the cloud platform 200 to retrieve the data in the cloud platform 200, and the cloud platform links other hospital terminal information files.
  • the database 400 may be located in the server 100 to facilitate access by the server 100.
  • the server 100 may be separately provided from the cloud platform 200, that is, the cloud platform 200 is isolated from the database 400. Only the authorized server 100 can access the database, which can improve the data security of the database.
  • the database 100 may include a processor 101, a storage device 102, a user interaction module 103, and a communication center 104.
  • the communication center 104 is used for communication between various components in the server 100, and the user interaction module 103 is used to receive information, instructions, etc. input by the user, such as a touch screen, a mouse, a keyboard, voice, face recognition, and the like.
  • the communication center 104 is used for the server 100 to communicate with the outside.
  • the network communication module 104 may include a wired interface and a wireless interface, such as an RS232 module, a radio frequency module, a WIFI module, and the like.
  • the storage device 102 may include one or more computer-readable storage media, and it includes not only an internal memory but also an external memory.
  • the memory stores an operating system, a holographic assisted diagnosis and treatment system, and the like.
  • the processor 101 is configured to call the holographic auxiliary diagnosis and treatment system and other components (for example, the user interaction module 103 and the communication center 104) in the memory 102, and provide holographic auxiliary diagnosis and treatment information to the doctor and the like.
  • FIG. 3 is a functional module schematic diagram of the first embodiment of the intelligent auxiliary diagnosis and treatment system provided by the present invention. As shown in FIG. 3, the intelligent assisted diagnosis and treatment system provided by this embodiment includes:
  • a receiving module 110 configured to receive a medical record
  • a classification module 120 configured to classify the received medical records according to the type of the disease, and store the classified medical records into a corresponding database;
  • the matching module 130 is configured to match the medical record with a medical record template in a medical record template library to obtain a medical record template with a matching degree greater than a preset threshold, wherein the medical record template stores a disease type and disease information corresponding to the disease type;
  • the auxiliary diagnosis module 140 is configured to obtain the matched disease type and the disease information corresponding to the disease type, use the artificial intelligence convolutional neural network learning method to give a corresponding historical diagnosis reference scheme, and output the matched disease type and related disease Type corresponding illness information for doctor's reference.
  • the receiving module 110 receives the medical records uploaded to the server 100 through the communication center 104; then the classification module 120 classifies the received medical records according to the type of the disease, and stores the classified medical records into the corresponding database; the matching module 130 Match the received medical records with the medical record templates in the medical record template library in the database 400 to obtain a disease type with a matching degree greater than a preset threshold.
  • a preset threshold there may be multiple disease types with a matching degree greater than the preset threshold. In this embodiment, a maximum of 10 types of diseases will be set. If the degree of matching is greater than a preset threshold, the types of diseases that meet the conditions will be sorted according to the degree of matching.
  • the auxiliary diagnosis module 140 extracts the disease information corresponding to the disease type from the auxiliary diagnosis information database in the database 400 according to the matched disease type, and uses an artificial intelligence convolutional neural network learning method to give a corresponding historical diagnosis reference scheme.
  • the extracted condition information is returned to the terminal for uploading the medical records through the network communication module 104. If the doctor according to the disease type and the disease type information corresponding to the disease type in the matched medical record template, if no matching disease is found, the auxiliary diagnosis module 140 uses an artificial intelligence convolutional neural network algorithm in conjunction with the expert opinion of the disease. After the type of disease, the auxiliary diagnosis and treatment plan corresponding to the corresponding disease is obtained for doctors' reference.
  • the above medical record template library also includes: standard medical record templates from evidence-based medicine, which includes 300 kinds of common and common diseases, and the medical record template library will be updated regularly.
  • the medical record template is classified according to the type of disease.
  • the information of the disease syndrome corresponding to each type of disease can include the main complaint, current medical history, past history, positive signs and laboratory test results, imaging test results, genetic test reports, vital signs, health reports and so on.
  • the intelligent auxiliary diagnosis and treatment system matches electronic medical records with medical record templates of historical medical record template libraries, and then uses artificial intelligence to learn the experience of each doctor and expert treatment plan.
  • the diagnosis and treatment methods for intelligent diagnosis and treatment of diseases, the combination of the most effective treatment methods for historically related diseases and the current treatment experience of each such expert are given to assist ordinary doctors in diagnosis. Therefore, this embodiment solves the problem of insufficient doctor level in current medical and health services, which not only can improve the doctor's medical level, but also effectively reduce the doctor's misdiagnosis rate.
  • FIG. 4 is a schematic diagram of functional modules of a second embodiment of the intelligent auxiliary diagnosis and treatment system provided by the present invention. Compared with the first embodiment shown in FIG. 3, the intelligent auxiliary diagnosis and treatment system provided by this embodiment further includes:
  • Conversion module 150 for converting text medical records into standardized electronic medical records through text and image analysis in accordance with artificial intelligence deep learning methods, and converting non-standardized electronic medical records and semi-normalized electronic medical records into standardized electronic medical records according to system preset rules ;
  • the artificial intelligence module 160 is configured to comprehensively compare a plurality of doctors' treatment schemes of a matched disease type according to a patient's disease type, and obtain a diagnosis and treatment scheme including corresponding disease information and similar disease information to transmit to the auxiliary diagnosis module.
  • the above-mentioned intelligent auxiliary diagnosis and treatment system can be applied to the in-hospital system to ensure the information security in the hospital, and at the same time, it can be linked to the network cloud platform for other affiliated hospitals to share data.
  • the textual medical records of patients transferred from other hospitals to this hospital can be obtained through the conversion module 150, or patient data obtained from other hospitals can be converted to standard medical records through identity verification, and uploaded to the receiving module to match the in-hospital database or Cloud platform database.
  • the medical record templates in the medical record template library are all in the same format, and the medical record templates provided by the hospital are usually not standardized medical record templates. Therefore, the first intelligent diagnosis and treatment system provided by this city can recognize electronic and text medical records into preset standard information modules through the image recognition function provided by the conversion module. The remaining information that does not match the module information will be processed using artificial intelligence module 160, and the classified information will be converted into other auxiliary information modules for intelligent auxiliary diagnosis and treatment.
  • the artificial intelligence module 140 can also be combined with other system information in the hospital through the intelligent interaction module 220 to learn through intelligent learning programs, and call relevant medical record information from other hospital systems in the hospital alliance for the artificial intelligence module to learn and judge, and give intelligent results. Then it is introduced into the auxiliary diagnosis module, and a doctor's auxiliary diagnosis and treatment plan is given according to the needs of the disease.
  • the intelligent assisted diagnosis and treatment system of this embodiment further includes:
  • a voice recognition module is used to collect and recognize a voice signal, convert the recognized voice information into text information, and record the corresponding voice information in an outpatient medical record template.
  • the server 100 may provide an access website or an access interface, and a doctor uses a computer, a mobile phone, or a wearable device terminal to access the access website or link to the access interface.
  • a doctor can log in to the intelligent assistant diagnosis and treatment system through the account and password and the face recognition function.
  • the doctor must register on the server 100 and pass the in-hospital review. The login can only be completed after the registration review is successful.
  • doctors can check the history information, diagnosis and treatment plan, follow-up process of treatment plan, and effective feedback of the plan for all patients.
  • FIG. 7 is a functional module schematic diagram of a third embodiment of the intelligent auxiliary diagnosis and treatment system provided by the present invention. Compared with the first embodiment shown in FIG. 3, the intelligent auxiliary diagnosis and treatment system provided by this embodiment further includes:
  • the tracking and monitoring module 170 is used to check whether there are unchecked items or undiagnosed processes in the medical record after the doctor determines the type of the patient's disease, and timely feedback the execution progress of the treatment plan in the subsequent treatment plan and give The treatment effect analysis report indicates whether the doctor needs to modify the treatment plan for the second auxiliary diagnosis and treatment during the treatment process.
  • the remote communication module 180 is configured to provide an external interface for communication between the intelligent auxiliary diagnosis and treatment system and the remote auxiliary system, and to receive a remote access request from a doctor, and establish communication with the remote assist system according to the access request, to perform remote online assistance.
  • the doctor's opinion will also be used to optimize the auxiliary diagnosis and treatment plan.
  • the doctor targets the disease, the doctor needs to check the items or according to the diagnostic steps required by evidence-based medicine to give the doctor the latest medical knowledge. If the medical operation of the relevant disease is not performed according to the evidence-based medicine, a prompt message is issued.
  • the tracking and monitoring module 170 can also check whether there are unchecked items or unchecked items in the electronic medical record after the doctor according to the matched disease type and the disease information corresponding to the disease type and the disease related type obtained through the artificial intelligence algorithm.
  • the process of diagnosis, timely feedback of the effectiveness of the treatment plan in the subsequent treatment plan, and a treatment efficiency analysis report are given, which prompts the doctor whether to modify the treatment plan for secondary auxiliary diagnosis and treatment during the treatment process.
  • the above-mentioned intelligent auxiliary diagnosis and treatment system further provides a remote online auxiliary function, that is, an external interface for remote auxiliary communication.
  • the remote communication module 180 will establish a connection with the remote assistance system through the external interface provided by the doctor according to the remote access request of the doctor.
  • the doctor can remotely connect with the patient or a related specialist, and can share the corresponding disease information, historical patient information, and diagnosis and treatment plan through the auxiliary diagnosis and treatment system.
  • FIG. 8 is a flowchart of a first embodiment of the intelligent assisted diagnosis and treatment method provided by the present invention. As shown in FIG. 8, the intelligent assisted diagnosis and treatment method provided by the present invention includes the following steps:
  • Step S110 receiving a medical record
  • Step S120 classify the received medical records according to the type of the disease, and store the classified medical records into a corresponding database;
  • Step S130 matching the medical record with the medical record template in the medical record template library to obtain a medical record template with a matching degree greater than a preset threshold, wherein the medical record template stores a disease type and disease information corresponding to the disease type;
  • Step S140 Obtain the matched disease type and the disease information corresponding to the disease type, use the artificial intelligence convolutional neural network learning method to give a corresponding historical diagnosis reference scheme, and output the matched disease type and the disease corresponding to the disease type Information for your doctor's reference.
  • the intelligent assisted diagnosis and treatment method provided in this embodiment matches electronic medical records with medical record templates of historical medical record template libraries, and then uses artificial intelligence to learn the experience of each doctor and expert treatment plan.
  • the diagnosis and treatment methods for intelligent diagnosis and treatment of diseases, the combination of the most effective treatment methods for historically related diseases and the current treatment experience of each such expert are given to assist ordinary doctors in diagnosis. Therefore, this embodiment solves the problem of insufficient doctor level in current medical and health services, which not only can improve the doctor's medical level, but also effectively reduce the doctor's misdiagnosis rate.
  • FIG. 9 is a flowchart of a second embodiment of the intelligent assisted diagnosis and treatment method provided by the present invention. As shown in FIG. 9, the intelligent assisted diagnosis and treatment method provided by the present invention includes the following steps:
  • Step S210 receiving a medical record
  • Step S220 Convert the medical record according to a preset conversion rule.
  • the text medical records are converted into standardized electronic medical records through text and image analysis according to artificial intelligence deep learning methods; if the received medical records are non-standardized electronic medical records and semi-standardized electronic medical records, Non-standardized electronic medical records and semi-normalized electronic medical records are converted into standardized electronic medical records according to the preset rules of the system.
  • the application of the above-mentioned intelligent auxiliary diagnosis and treatment system can be used with the alliance hospital of the hospital. Through this step, any form of medical records (electronic medical records, text medical records) provided by the Alliance Hospital can be converted into standardized electronic medical records and stored in the cloud platform.
  • the electronic medical record templates in the medical record template library have a unified format, and the medical records provided by the hospital of the Alliance are usually not standardized medical records. Therefore, when the intelligent assistant diagnosis and treatment system receives the medical records uploaded by the alliance hospital, it will use artificial intelligence to automatically identify whether the current file is a standardized medical record. If the received electronic medical record is not a standard medical record, the medical record of the alliance hospital is converted into a standard medical record according to a preset conversion rule.
  • Step S230 classify the received medical records according to the type of the disease, and store the classified medical records into a corresponding database;
  • Step S240 matching the medical record with a medical record template in a medical record template library to obtain a medical record template with a matching degree greater than a preset threshold, wherein the medical record template stores a disease type and disease information corresponding to the disease type;
  • Step S250 Obtain the matched disease type and the disease information corresponding to the disease type, use the artificial intelligence convolutional neural network learning method to give a corresponding historical diagnosis reference scheme, and output the matched disease type and the disease corresponding to the disease type Information for your doctor's reference.
  • Step S260 According to the type of the patient's disease, a plurality of doctors comprehensively compare the treatment schemes of the matched disease types, and obtain a diagnosis and treatment plan including the corresponding disease information and similar disease information, and transmit it to the doctor.
  • Step S270 After the doctor determines the type of the patient's disease, check whether there are unchecked items or undiagnosed processes in the medical history, and timely feedback the implementation progress of the treatment plan in the subsequent treatment plan and give a treatment effect analysis report , Prompting the doctor whether to modify the treatment plan for the second auxiliary diagnosis and treatment during the treatment process.
  • the above-mentioned intelligent auxiliary diagnosis and treatment system also provides diagnosis and treatment assistance information corresponding to the type of the disease.
  • the information will be displayed on the terminal screen for doctors to check.
  • the intelligent auxiliary diagnosis and treatment system obtains the assistance corresponding to the disease type from the database.
  • the diagnosis and treatment plan, and the diagnosis and treatment plan obtained by using the artificial intelligence convolutional neural network algorithm, such as the currently missing inspection items for the disease, the treatment process, the medication plan, the diagnosis and treatment plan, and so on.
  • the doctor determines the disease type of the patient according to the disease type and the disease information corresponding to the disease type in the matched medical record template library, and then performs an auxiliary diagnosis and treatment scheme provided by the artificial intelligence convolutional neural network algorithm for the doctor's reference. , To further assist doctors in making judgments.
  • this embodiment can also provide data on historical treatment plans and treatment effects for this type of disease.
  • the above-mentioned intelligent assisted diagnosis and treatment system linked database can view a treatment plan report shared by other doctors whose current treatment effectiveness is greater than a preset threshold for doctors' reference.
  • FIG. 10 is a flowchart of a third embodiment of the intelligent assisted diagnosis and treatment method provided by the present invention. As shown in FIG. 10, compared to the first embodiment shown in FIG. 8, the intelligent assisted diagnosis and treatment method provided by this embodiment further includes: The following steps:
  • the above-mentioned intelligent auxiliary diagnosis and treatment system provides a remote online assistance function, that is, provides an external interface for communication with the remote assistance system.
  • the intelligent assistant diagnosis and treatment system will establish a link with the remote assistant system through the external interface provided by the doctor according to the remote access request of the doctor.
  • the doctor can remotely assist the assistance of a more professional doctor for the disease, including the sharing of the vital signs of the disease and other corresponding signs or surgical procedures, etc. Remote assistance for surgery, etc.
  • an outpatient medical record template receive the doctor's voice signal, recognize the collected voice signal, convert the recognized voice information into text information, and record it into the corresponding position in the outpatient medical record template to generate a medical record.

Abstract

La présente invention concerne un système de diagnostic et de traitement auxiliaires intelligents, comprenant : un module de réception, conçu pour recevoir des dossiers médicaux; un module de classement, conçu pour classer les dossiers médicaux reçus selon les types de maladie, et stocker les dossiers médicaux classés dans une base de données correspondante; un module de mise en correspondance, conçu pour mettre en correspondance des dossiers médicaux avec des modèles de dossiers médicaux dans une bibliothèque de modèles de dossiers médicaux, de façon à acquérir un modèle de dossier médical ayant un degré de mise en correspondance supérieur à un seuil prédéfini, les modèles de dossiers médicaux stockant les types de maladie et les informations de symptômes correspondant aux types de maladie; un module de diagnostic auxiliaire, conçu pour acquérir un type de maladie apparié et des informations de symptômes correspondant au type de maladie, utiliser un procédé d'apprentissage de réseau neuronal convolutionnel à intelligence artificielle pour fournir un schéma de diagnostic historique correspondant pour référence, et délivrer en sortie le type de maladie apparié et les informations de symptômes correspondant au type de maladie pour référence à un médecin. La présente invention résout le problème, dans des services médicaux et de santé actuels, d'une capacité insuffisante de médecins, améliorant le niveau médical de médecins, et réduisant efficacement le taux de mauvais diagnostic de médecins.
PCT/CN2018/101271 2018-08-20 2018-08-20 Système et procédé de diagnostic et de traitement auxiliaires intelligents WO2020037454A1 (fr)

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CN201880008002.1A CN110249392A (zh) 2018-08-20 2018-08-20 智能辅助诊疗系统及方法

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