WO2020037454A1 - Smart auxiliary diagnosis and treatment system and method - Google Patents

Smart auxiliary diagnosis and treatment system and method 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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WO
WIPO (PCT)
Prior art keywords
disease
treatment
diagnosis
medical records
medical record
Prior art date
Application number
PCT/CN2018/101271
Other languages
French (fr)
Chinese (zh)
Inventor
张�雄
韩恩莉
舒振峰
石宇
Original Assignee
深圳市全息医疗科技有限公司
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Filing date
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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/en
Priority to CN201880008002.1A priority patent/CN110249392A/en
Publication of WO2020037454A1 publication Critical patent/WO2020037454A1/en

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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

The present invention provides a smart auxiliary diagnosis and treatment system, comprising: a receiving module, configured to receive medical records; a classifying module, configured to classify the received medical records according to the types of disease, and store the classified medical records in a corresponding database; a matching module, configured to match medical records with medical record templates in a medical record template library, so as to acquire a medical record template having a matching degree greater than a preset threshold, the medical record templates storing the types of disease and symptoms information corresponding to the types of disease; an auxiliary diagnosis module, configured to acquire a matched type of disease and symptoms information corresponding to the type of disease, use an artificial intelligence convolutional neural network learning method to provide a corresponding historical diagnostic scheme for reference, and output the matched type of disease and the symptoms information corresponding to the type of disease for reference of a doctor. The present invention solves the problem, in current medical and health services, of inadequate capability of doctors, improving the medical level of doctors, and effectively reducing the misdiagnosis rate of doctors.

Description

智能辅助诊疗系统及方法Intelligent auxiliary diagnosis and treatment system and method 技术领域Technical field
本发明涉及医疗信息化技术领域，尤其涉及一种智能辅助诊疗系统及方法。The present invention relates to the field of medical information technology, and in particular, to an intelligent auxiliary diagnosis and treatment system and method.
背景技术Background technique
随着科技的发展，特别是互联网、人工智能、大数据的出现，改变了世界，改变了社会的方方面面，深刻的影响着我们生活的各个领域。随着医疗技术的发展，医疗信息量大，种类繁多，各种信息格式不同（文字、数字、图像、影音），医生需要查阅大量信息，才能给出相应诊疗方案，但由于信息量庞大，难免会有遗漏并给出不全面的诊疗方案。医院医疗质量管理上需要能方便快捷的管理种类繁多的医疗信息。病人需要得到医生详细的病程记录来保障自己的切身利益。这些需求的提出，使得在卫生医疗行业必须有可靠的医院电子诊疗辅助系统来帮助完成。基于电子病历的医疗辅助系统可以最大程度的满足以上的需求，并可以大大提高医疗行业的工作质量及效率。With the development of science and technology, especially the emergence of the Internet, artificial intelligence, and big data, it has changed the world, changed all aspects of society, and profoundly affected all areas of our lives. With the development of medical technology, there is a large amount of medical information, many types, and various information formats (text, numbers, images, audio-visual). Doctors need to consult a large amount of information in order to give a corresponding diagnosis and treatment plan. There will be omissions and incomplete treatment plans. Hospital medical quality management needs to be able to conveniently and quickly manage a wide range of medical information. Patients need to get detailed medical records of doctors to protect their vital interests. These demands make it necessary to have a reliable hospital electronic diagnostic assistance system in the health care industry to help accomplish this. Electronic medical records-based medical assistance systems can meet the above requirements to the greatest extent, and can greatly improve the quality and efficiency of work in the medical industry.
技术问题technical problem
本发明的目的在于提供一种智能辅助诊疗系统及方法以实现对患者进行精准的诊断治疗，较大的提高医院的诊疗水平和工作效率。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.
技术解决方案Technical solutions
一方面，本发明实施例提供一种智能辅助诊疗系统，其特征在于，包括：In one aspect, an embodiment of the present invention provides an intelligent assisted diagnosis and treatment system, which is characterized by including:
接收模块，用于接收病历；Receiving module for receiving medical records;
分类模块，用于根据疾病类型将接收到的所述病历进行分类处理，并将分类后的所述病历存储到相应的数据库；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;
匹配模块，用于将所述病历与病历模板库中的病历模板进行匹配，获取匹配度大于预设阈值的病历模板，其中病历模板存储疾病类型及与疾病类型对应的病症信息；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;
辅助诊断模块，用于获取所匹配的疾病类型及与疾病类型对应的病症信息，利用人工智能卷积神经网络学习方法给出相应的历史诊断参考方案，并输出所匹配的疾病类型及与疾病类型对应的病症信息，以供医生参考。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.
优选地，还包括：Preferably, it further comprises:
转换模块，用于通过文字及图像分析按照人工智能深度学习方法将文本病历转换成规范化的电子病历，将非规范化的电子病历及半规范化的电子病历按照系统预设规则转换成规范化的电子病历。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.
优选地，还包括：Preferably, it further comprises:
人工智能模块，用于根据患者的疾病类型将多名医生对所匹配的疾病类型的治疗方案进行综合比对，得到包括对应病症信息及类似病症信息的诊疗方案传送至辅助诊断模块。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.
优选地，还包括：Preferably, it further comprises:
跟踪监测模块，用于在医生确定病人的疾病类型后，检查病历中是否还存在未被检查的项目或未被诊断的流程，在后续的治疗方案中及时反馈治疗方案的执行进度并给出治疗效果分析报告，提示医生是否在治疗过程中需要修改治疗方案进行二次辅助诊疗。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.
优选地，还包括：Preferably, it further comprises:
远程通讯模块，用于提供智能辅助诊疗系统与远程辅助系统通讯的外部接口，以及接收医生的远程访问请求，并根据访问请求与远程辅助系统建立通讯，以进行远程在线辅助。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.
优选地，还包括：Preferably, it further comprises:
语音识别模块，用于采集的语音信号并进行识别，将识别的语音信息转换为文本信息，并录入至门诊病历模板中的相应位置。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.
相应地，本发明还提供一种智能辅助诊疗方法，包括以下步骤：Accordingly, the present invention also provides an intelligent assisted diagnosis and treatment method, which includes the following steps:
接收病历；Receiving medical records;
根据疾病类型将接收到的所述病历进行分类处理，并将分类后的所述病历存储到相应的数据库；Classify the received medical records according to the type of disease, and store the classified medical records into a corresponding database;
将所述病历与病历模板库中的病历模板进行匹配，获取匹配度大于预设阈值的病历模板，其中病历模板存储疾病类型及与疾病类型对应的病症信息；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;
获取所匹配的疾病类型及与疾病类型对应的病症信息，利用人工智能卷积神经网络学习方法给出相应的历史诊断参考方案，并输出所匹配的疾病类型及与疾病类型对应的病症信息，以供医生参考。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.
优选地，所述病历包括电子病历和/或文档病历，在接收病历之后还包括：Preferably, the medical records include electronic medical records and / or file medical records, and after receiving the medical records, they further include:
若接收的病历为文档病历，通过文字及图像分析按照人工智能深度学习方法将文本病历转换成规范化的电子病历；If the received medical records are document medical records, the text medical records are converted into standardized electronic medical records through text and image analysis according to artificial intelligence deep learning methods;
若接收的病历为非规范化的电子病历及半规范化的电子病历，将非规范化的电子病历及半规范化的电子病历按照系统预设规则转换成规范化的电子病历。If 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.
优选地，在获取所匹配的疾病类型及与疾病类型对应的病症信息，利用人工智能卷积神经网络学习方法给出相应的历史诊断参考方案，并输出所匹配的疾病类型及与疾病类型对应的病症信息，以供医生参考的所述步骤之后还包括：Preferably, after obtaining the matched disease type and the disease information corresponding to the disease type, using the artificial intelligence convolutional neural network learning method to give a corresponding historical diagnosis reference scheme, and output the matched disease type and the corresponding disease type The condition information for your doctor's reference is followed by:
根据患者的疾病类型将多名医生对所匹配的疾病类型的治疗方案进行综合比对，得到包括对应病症信息及类似病症信息的诊疗方案传送至医生。According to the patient's disease type, 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.
优选地，还包括：Preferably, it further comprises:
在医生确定病人的疾病类型后，检查病历中是否还存在未被检查的项目或未被诊断的流程，在后续的治疗方案中及时反馈治疗方案的执行进度并给出治疗效果分析报告，提示医生是否在治疗过程中需要修改治疗方案进行二次辅助诊疗。After the doctor determines the type of patient's disease, check whether there are unchecked items or undiagnosed processes in the medical history, and timely feedback the progress of the implementation of the treatment plan in the subsequent treatment plan and give a treatment effect analysis report to remind the doctor Whether it is necessary to modify the treatment plan in the course of treatment for secondary auxiliary diagnosis and treatment.
优选地，还包括：Preferably, it further comprises:
接收医生的远程访问请求，并根据该访问请求与远程辅助系统建立通讯，以进行远程在线辅助。Receive a doctor's remote access request and establish communication with the remote assistance system according to the access request for remote online assistance.
优选地，还包括：Preferably, it further comprises:
提供门诊病历模板，接收医生的语音信号，对所采集的语音信号进行识别，将识别的语音信息转换为文本信息，并录入至门诊病历模板中的相应位置，生成病历。Provide 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.
有益效果Beneficial effect
实施本发明实施例，具有如下有益效果：本发明提供的智能辅助诊疗系统和方法，通过电子病历与历史病历模板库的病历模板进行匹配，再利用人工智能学习各个医生专家治疗方案的经验。对病症进行智能诊疗的诊疗方法，给出历史相关病症最有效的治疗方法及目前各个此类专家的经验的治疗方案的综合，来辅助普通医生进行诊断。因此，本发明例解决了目前医疗卫生服务中，医生水平不足的问题，不但可以提高医生的医疗水平，而且有效减少了医生的误诊率。Implementing the embodiments of the present invention has the following beneficial effects: 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.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to more clearly explain 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 introduced below. Obviously, the drawings in the following description are merely These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without paying creative labor.
图1为本发明智能辅助诊疗系统应用于医院的服务器时与医院终端的交互示意图；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;
图2为本发明智能辅助诊疗系统所应用的服务器的硬件架构示意图；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.
图3为本发明提供的智能辅助诊疗系统的第一实施例的功能模块示意图；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.
图4为本发明提供的智能辅助诊疗系统的第二实施例的功能模块示意图；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;
图5为本发明提供的智能辅助诊疗系统的登陆界面示意图；5 is a schematic diagram of a login interface of the intelligent assisted diagnosis and treatment system provided by the present invention;
图6为本发明提供的智能辅助诊疗系统登录后的界面示意图；6 is a schematic diagram of an interface of the intelligent assisted diagnosis and treatment system provided by the present invention after login;
图7为本发明提供的智能辅助诊疗系统的第三实施例的功能模块示意图；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;
图8为本发明提供的智能辅助诊疗方法的第一实施例的流程图；8 is a flowchart of a first embodiment of a method for intelligently assisted diagnosis and treatment provided by the present invention;
图9为本发明提供的智能辅助诊疗方法的第二实施例的流程图；9 is a flowchart of a second embodiment of a method for intelligently assisted diagnosis and treatment provided by the present invention;
图10为本发明提供的智能辅助诊疗方法的第三实施例的流程图。FIG. 10 is a flowchart of a third embodiment of the intelligent assisted diagnosis and treatment method provided by the present invention.
本发明的实施方式Embodiments of the invention
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。In the following, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.
如图1所示，本发明的智能辅助诊疗系统可设置于服务器100中。该服务器100可供院内终端500（例如，各类医院的电脑终端、诊疗终端等等）及通过云平台200链接的其他医院终端300访问。各类医院可以通过网络云平台200与服务器100进行通讯。即该服务器100可以位于医院的局域网中，并可供该医院下属的其他医院通过广域网或移动互联网进行访问。若要访问数据库400，必须经有效的身份验证，其中包括医生面部识别，身份信息密码登陆或语音识别，只有通过身份验证的用户或终端才可以访问该数据库400。As shown in FIG. 1, 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. To access the database 400, 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.
服务器100还具有数据库访问功能，该数据库400用于存储各类数据，例如医学知识、患者诊断信息、医生诊断经验以及治疗方案等等。当服务器100接收到用户或终端的处理需求后，根据终端需求想获取更多病症信息比对，还可访问云平台200，以调取云平台200中的资料，云平台链接其他医院终端信息档案，供医生使用。本实施例中，该数据库400可以位于服务器100中，便于服务器100访问。当然，另一实施例中，该服务器100还可以与云平台200之间分开设置，即将云平台200与数据库400形成隔离，只有授权的服务器100才能访问该数据库，如此可以提高数据库的数据安全。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. After 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. For doctors. In this embodiment, the database 400 may be located in the server 100 to facilitate access by the server 100. Of course, in another embodiment, 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.
如图2所示，上述数据库100可包括处理器101、存储设备102、用户交互模块103、通讯中心104。通讯中心104用于服务器100中各组成部件之间的通信，用户交互模块103用于接收用户输入的信息、指令等等，例如触摸屏、鼠标、键盘、语音、人脸识别等。通讯中心104用于服务器100与外部进行互相通信，该网络通讯模块104可包括有线接口和无线接口，例如RS232模块、射频模块、WIFI模块等等。存储设备102可以包括一个或一个以上计算机可读存储介质，而且其不但包括内部存储器，还包括外部存储器。该存储器中存储有操作系统及全息辅助诊疗系统等等。处理器101用于调用存储器102中的全息辅助诊疗系统以及其他组建（例如，用户交互模块103、通讯中心104），为医生提供全息的辅助诊疗信息等等。As shown in FIG. 2, 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.
图3为本发明提供的智能辅助诊疗系统的第一实施例的功能模块示意图。如图3所示，本实施例提供的智能辅助诊疗系统包括：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:
接收模块110，用于接收病历；A receiving module 110, configured to receive a medical record;
分类模块120，用于根据疾病类型将接收到的所述病历进行分类处理，并将分类后的所述病历存储到相应的数据库；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;
匹配模块130，用于将所述病历与病历模板库中的病历模板进行匹配，获取匹配度大于预设阈值的病历模板，其中病历模板存储疾病类型及与疾病类型对应的病症信息；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;
辅助诊断模块140，用于获取所匹配的疾病类型及与疾病类型对应的病症信息，利用人工智能卷积神经网络学习方法给出相应的历史诊断参考方案，并输出所匹配的疾病类型及与疾病类型对应的病症信息，以供医生参考。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.
上述接收模块110通过通讯中心104接收上传至服务器100中的病历；然后该分类模块120根据疾病类型将接收到的病历进行分类处理，并将分类后的所述病历存储到相应的数据库；匹配模块130将接收到的病历与数据库400中的病历模板库的病历模板进行匹配获得匹配度大于预设阈值的疾病类型，这里匹配度大于预设阈值的疾病类型可能有多个。本实施例中，将设置匹配的疾病类型最多为10种，若匹配度大于预设阈值的疾病类型时，将满足条件的疾病类型按照匹配度从大到小进行排序。该辅助诊断模块140根据匹配的疾病类型向数据库400中的辅助诊断信息库提取与该疾病类型对应的病症信息，利用人工智能卷积神经网络学习方法给出相应的历史诊断参考方案，同时，将所提取的病症信息通过网络通讯模块104返回至上传病历的终端。如果在医生根据匹配的病历模板中的疾病类型及疾病类型对应的病症信息，如未发现与之匹配的病症，则辅助诊断模块140通过人工智能卷积神经网络算法，结合此病症专家意见给出的疾病类型后，获取相应疾病对应的辅助诊疗方案，供医生参考。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. Here, 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.
上述病历模板库还包括：来自于循证医学的标准病历模板，其中包括300种全科常见多发病，而且该病历模板库将定时更新。病历模板按疾病类型分类，每类疾病类型对应的病证的信息可包括主诉、现病史、既往史、阳性体征及化验检查结果、影像检查结果、基因检测报告、生命体征、健康报告等等。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.
本实施例提供的智能辅助诊疗系统，通过电子病历与历史病历模板库的病历模板进行匹配，再利用人工智能学习各个医生专家治疗方案的经验。对病症进行智能诊疗的诊疗方法，给出历史相关病症最有效的治疗方法及目前各个此类专家的经验的治疗方案的综合，来辅助普通医生进行诊断。因此，本实施例解决了目前医疗卫生服务中，医生水平不足的问题，不但可以提高医生的医疗水平，而且有效减少了医生的误诊率。The intelligent auxiliary diagnosis and treatment system provided by 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.
图4为本发明提供的智能辅助诊疗系统的第二实施例的功能模块示意图。与图3所示的第一实施例相比，本实施例提供的智能辅助诊疗系统还包括：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:
转换模块150，用于通过文字及图像分析按照人工智能深度学习方法将文本病历转换成规范化的电子病历，将非规范化的电子病历及半规范化的电子病历按照系统预设规则转换成规范化的电子病历；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 ;
人工智能模块160，用于根据患者的疾病类型将多名医生对所匹配的疾病类型的治疗方案进行综合比对，得到包括对应病症信息及类似病症信息的诊疗方案传送至辅助诊断模块。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.
上述基于智能辅助诊疗系统可应用于院内系统，以保障院内信息安全，并同时可链接网络云平台供其他联盟医院共享数据。如图3所示，通过转换模块150可获取病患从其它医院转院到此医院的文本病历或通过身份验证从其它医院获取病患资料转换为标准的病历，上传至接收模块后匹配院内数据库或云平台数据库。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. As shown in FIG. 3, 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.
为了便于病历模板对电子病历的疾病类型匹配，病历模板库中的案子病历模板均为同一格式，而医院提供的病历模板通常不是规范的病历模板。因此本市首例提供的智能诊疗系统通过转换模块提供的图像识别功能，可以识别电子及文本病历转化为预置的标准信息模块。其余未与模块信息匹配的信息将使用人工智能模块160进行数据处理，分类信息将转化为智能辅助诊疗的其他辅助信息模块。人工智能模块140还可以通过智能交互模块220与院内其他系统信息进行合并通过智能学习方案进行学习，从医院联盟体的其他院内系统调用相关病历信息供人工智能模块进行学习及判断，给出智能结果后传入辅助诊断模块，根据病症需求给出医生辅助诊疗方案。In order to facilitate the matching of the medical record template to the disease type of the electronic medical record, 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.
进一步地，本实施例的智能辅助诊疗系统还包括：Further, the intelligent assisted diagnosis and treatment system of this embodiment further includes:
语音识别模块，用于采集的语音信号并进行识别，将识别的语音信息转换为文本信息，并录入至门诊病历模板中的相应位置。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.
进一步地，上述服务器100可以提供访问网址或访问接口，医生利用电脑、手机、穿戴设备终端访问该访问网址或链接该访问接口。如图5所示，医生可以通过账号和密码以及人脸识别功能登陆智能辅助诊疗系统，医生必须在服务器100上注册，并通过院内审核，注册审核成功后才能登陆。如图6所示，登陆全息辅助诊疗系统后，医生可查阅所有病人病症历史信息、诊疗方案，治疗方案跟踪过程，方案有效率反馈。Further, 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. As shown in FIG. 5, the 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. As shown in Figure 6, after logging into the holographic assisted diagnosis and treatment system, 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.
图7为本发明提供的智能辅助诊疗系统的第三实施例的功能模块示意图。与图3所示的第一实施例相比，本实施例提供的智能辅助诊疗系统还包括：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:
跟踪监测模块170，用于在医生确定病人的疾病类型后，检查病历中是否还存在未被检查的项目或未被诊断的流程，在后续的治疗方案中及时反馈治疗方案的执行进度并给出治疗效果分析报告，提示医生是否在治疗过程中需要修改治疗方案进行二次辅助诊疗。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.
远程通讯模块180，用于提供智能辅助诊疗系统与远程辅助系统通讯的外部接口，以及接收医生的远程访问请求，并根据访问请求与远程辅助系统建立通讯，以进行远程在线辅助。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.
本实施例中，还将利用医生的意见对辅助诊疗方案进行优化，医生在针对该疾病时，需要检查的项目或根据循证医学所需要的诊断步骤，给与医生最新的医学知识的参考，如未按照循证医学进行的相关病症的医学操作，则发出提示信息。该跟踪监测模块170还可以在医生根据所匹配的疾病类型及疾病类型对应的病症信息及通过人工智能算法获取的疾病相关类型后，检查该电子病历中是否还存在未被检查的项目或未被诊断的流程，在后续的治疗方案中及时反馈治疗方案的有效率并给出治疗有效率分析报告，提示医生是否在治疗过程中需要修改治疗方案进行二次辅助诊疗。In this embodiment, the doctor's opinion will also be used to optimize the auxiliary diagnosis and treatment plan. When 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.
在本实施例中，上述智能辅助诊疗系统还提供了远程在线辅助功能，即提供远程辅助通讯的外部接口。远程通讯模块180将根据医生的远程访问请求，通过其提供的外部接口与远程辅助系统建立连接。本实施例中，医生通过智能辅助诊疗系统与该远程辅助系统建立链接后，医生可以远程连接病患或者相关专科医生，并可通过辅助诊疗系统共享相应病症信息以及历史病患信息及诊疗方案。In this embodiment, 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. In this embodiment, after the doctor establishes a link with the remote auxiliary system through the intelligent auxiliary diagnosis and treatment system, 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.
相应地，本发明还提供一种智能辅助诊疗方法。图8为本发明提供的智能辅助诊疗方法的第一实施例的流程图，如图8所示，本发明提供的智能辅助诊疗方法包括以下步骤：Correspondingly, the present invention also provides an intelligent assisted diagnosis and treatment method. 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:
步骤S110：接收病历；Step S110: receiving a medical record;
步骤S120：根据疾病类型将接收到的所述病历进行分类处理，并将分类后的所述病历存储到相应的数据库；Step S120: classify the received medical records according to the type of the disease, and store the classified medical records into a corresponding database;
步骤S130：将所述病历与病历模板库中的病历模板进行匹配，获取匹配度大于预设阈值的病历模板，其中病历模板存储疾病类型及与疾病类型对应的病症信息；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;
步骤S140：获取所匹配的疾病类型及与疾病类型对应的病症信息，利用人工智能卷积神经网络学习方法给出相应的历史诊断参考方案，并输出所匹配的疾病类型及与疾病类型对应的病症信息，以供医生参考。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.
本实施例提供的智能辅助诊疗方法，通过电子病历与历史病历模板库的病历模板进行匹配，再利用人工智能学习各个医生专家治疗方案的经验。对病症进行智能诊疗的诊疗方法，给出历史相关病症最有效的治疗方法及目前各个此类专家的经验的治疗方案的综合，来辅助普通医生进行诊断。因此，本实施例解决了目前医疗卫生服务中，医生水平不足的问题，不但可以提高医生的医疗水平，而且有效减少了医生的误诊率。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.
图9为本发明提供的智能辅助诊疗方法的第二实施例的流程图，如图9所示，本发明提供的智能辅助诊疗方法包括以下步骤：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:
步骤S210：接收病历；Step S210: receiving a medical record;
步骤S220：按照预置的转换规则，对病历进行转化。Step S220: Convert the medical record according to a preset conversion rule.
具体地，若接收的病历为文档病历，通过文字及图像分析按照人工智能深度学习方法将文本病历转换成规范化的电子病历；若接收的病历为非规范化的电子病历及半规范化的电子病历，将非规范化的电子病历及半规范化的电子病历按照系统预设规则转换成规范化的电子病历。上述智能辅助诊疗系统应用，可与医院的联盟医院使用。通过本步骤，可将联盟医院提供的任何形式的病历（电子病历、文本病历）转换为规范化的电子病历存储于云平台。为了便于智能辅助诊疗系统对电子病历的疾病类型匹配，病历模板库中的电子病历模板为统一格式，而联盟该医院提供的病历通常不是规范病历。因此，当智能辅助诊疗系统接收到联盟医院上传的病历后，将利用人工智能自动识别当前档案是否为规范病历。若接收到的电子病历不是规范病历，则按照预先设置的转换规则，将联盟医院的病历转换为规范病历。Specifically, if the received medical records are document medical records, 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. In order to facilitate the intelligent auxiliary diagnosis and treatment system to match the disease types of electronic medical records, 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.
步骤S230：根据疾病类型将接收到的所述病历进行分类处理，并将分类后的所述病历存储到相应的数据库；Step S230: classify the received medical records according to the type of the disease, and store the classified medical records into a corresponding database;
步骤S240：将所述病历与病历模板库中的病历模板进行匹配，获取匹配度大于预设阈值的病历模板，其中病历模板存储疾病类型及与疾病类型对应的病症信息；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;
步骤S250：获取所匹配的疾病类型及与疾病类型对应的病症信息，利用人工智能卷积神经网络学习方法给出相应的历史诊断参考方案，并输出所匹配的疾病类型及与疾病类型对应的病症信息，以供医生参考。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.
步骤S260：根据患者的疾病类型将多名医生对所匹配的疾病类型的治疗方案进行综合比对，得到包括对应病症信息及类似病症信息的诊疗方案传送至医生。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.
步骤S270：在医生确定病人的疾病类型后，检查病历中是否还存在未被检查的项目或未被诊断的流程，在后续的治疗方案中及时反馈治疗方案的执行进度并给出治疗效果分析报告，提示医生是否在治疗过程中需要修改治疗方案进行二次辅助诊疗。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.
具体地，上述智能辅助诊疗系统还提供了疾病类型相应的诊疗辅助信息。上述智能辅助诊疗系统所匹配的病历模板中的疾病类型及疾病类型所对应的病症信息后，该信息将显示在终端屏幕上，供医生进行查看。医生根据显示的疾病类型及疾病类型对应的病症信息，而后根据人工智能卷积神经网络算法得出的结果，确定患者的疾病类型后，智能辅助诊疗系统则从数据库中获取该疾病类型对应的辅助诊疗方案，并给出利用人工智能卷积神经网络算法获取的诊疗方案，例如该疾病当前缺失的检查项目，治疗流程，用药方案，诊疗方案等等。Specifically, the above-mentioned intelligent auxiliary diagnosis and treatment system also provides diagnosis and treatment assistance information corresponding to the type of the disease. After the disease type and the disease information corresponding to the disease type in the medical record template matched by the above-mentioned intelligent auxiliary diagnosis and treatment system, the information will be displayed on the terminal screen for doctors to check. After the doctor determines the disease type of the patient according to the displayed disease type and the disease information corresponding to the disease type, and then according to the results obtained by the artificial intelligence convolutional neural network algorithm, 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.
在本实施例中，医生根据所匹配的病历模板库中的疾病类型及疾病类型对应的病症信息，确定患者的疾病类型后，进行人工智能卷积神经网络算法提供的辅助诊疗方案，供医生参考，进一步地辅助医生进行判断。另外，本实施例还可以给与该疾病类型的历史治疗方案及治疗效果的数据。而且，上述智能辅助诊疗系统链接数据库可查看其他医生所共享的当前治疗有效率大于预设阈值的治疗方案报告，供医生参考。In this embodiment, 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. In addition, this embodiment can also provide data on historical treatment plans and treatment effects for this type of disease. In addition, 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.
图10为本发明提供的智能辅助诊疗方法的第三实施例的流程图，如图10所示，相较于图8所示的第一实施例，本实施例提供的智能辅助诊疗方法还包括以下步骤：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:
接收医生的远程访问请求，并根据该访问请求与远程辅助系统建立通讯，以进行远程在线辅助。Receive a doctor's remote access request and establish communication with the remote assistance system according to the access request for remote online assistance.
具体地，上述智能辅助诊疗系统提供远程在线辅助功能，即提供与远程辅助系统通讯的外部接口。智能辅助诊疗系统将根据医生的远程访问请求，通过其提供的外部接口与远程辅助系统建立链接。本实施例中，医生通过智能辅助诊疗系统与该远程辅助系统建立连接后，医生可以远程协助对此病症的更专业的医生的帮助，其中包括病症生命体征以及其他相应体征或手术过程等共享，手术远程协助等。Specifically, 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. In this embodiment, after the doctor establishes a connection with the remote assistance system through the intelligent auxiliary diagnosis and treatment system, 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.
进一步地，在本发明的其他优选实施例中，还包括以下步骤：Further, in other preferred embodiments of the present invention, the following steps are further included:
提供门诊病历模板，接收医生的语音信号，对所采集的语音信号进行识别，将识别的语音信息转换为文本信息，并录入至门诊病历模板中的相应位置，生成病历。Provide 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.
以上所揭露的仅为本发明一种较佳实施例而已，当然不能以此来限定本发明之权利范围，本领域普通技术人员可以理解实现上述实施例的全部或部分流程，并依本发明权利要求所作的等同变化，仍属于发明所涵盖的范围。What has been disclosed above is only a preferred embodiment of the present invention, and of course, the scope of rights of the present invention cannot be limited by this. Those of ordinary skill in the art can understand all or part of the process of implementing the above embodiments and follow the rights of the present invention. Equivalent changes required are still within the scope of the invention.

Claims (12)

  1. 一种智能辅助诊疗系统，其特征在于，包括：An intelligent assisted diagnosis and treatment system is characterized in that it includes:
    接收模块，用于接收病历；Receiving module for receiving medical records;
    分类模块，用于根据疾病类型将接收到的所述病历进行分类处理，并将分类后的所述病历存储到相应的数据库；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;
    匹配模块，用于将所述病历与病历模板库中的病历模板进行匹配，获取匹配度大于预设阈值的病历模板，其中病历模板存储疾病类型及与疾病类型对应的病症信息；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;
    辅助诊断模块，用于获取所匹配的疾病类型及与疾病类型对应的病症信息，利用人工智能卷积神经网络学习方法给出相应的历史诊断参考方案，并输出所匹配的疾病类型及与疾病类型对应的病症信息，以供医生参考。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.
  2. 根据权利要求1所述的智能辅助诊疗系统，其特征在于，还包括：The intelligent auxiliary diagnosis and treatment system according to claim 1, further comprising:
    转换模块，用于通过文字及图像分析按照人工智能深度学习方法将文本病历转换成规范化的电子病历，将非规范化的电子病历及半规范化的电子病历按照系统预设规则转换成规范化的电子病历。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.
  3. 根据权利要求1所述的智能辅助诊疗系统，其特征在于，还包括：The intelligent auxiliary diagnosis and treatment system according to claim 1, further comprising:
    人工智能模块，用于根据患者的疾病类型将多名医生对所匹配的疾病类型的治疗方案进行综合比对，得到包括对应病症信息及类似病症信息的诊疗方案传送至辅助诊断模块。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.
  4. 根据权利要求1所述的智能辅助诊疗系统，其特征在于，还包括：The intelligent auxiliary diagnosis and treatment system according to claim 1, further comprising:
    跟踪监测模块，用于在医生确定病人的疾病类型后，检查病历中是否还存在未被检查的项目或未被诊断的流程，在后续的治疗方案中及时反馈治疗方案的执行进度并给出治疗效果分析报告，提示医生是否在治疗过程中需要修改治疗方案进行二次辅助诊疗。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.
  5. 根据权利要求1所述的智能辅助诊疗系统，其特征在于，还包括：The intelligent auxiliary diagnosis and treatment system according to claim 1, further comprising:
    远程通讯模块，用于提供智能辅助诊疗系统与远程辅助系统通讯的外部接口，以及接收医生的远程访问请求，并根据访问请求与远程辅助系统建立通讯，以进行远程在线辅助。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.
  6. 根据权利要求1所述的智能辅助诊疗系统，其特征在于，还包括：The intelligent auxiliary diagnosis and treatment system according to claim 1, further comprising:
    语音识别模块，用于采集的语音信号并进行识别，将识别的语音信息转换为文本信息，并录入至门诊病历模板中的相应位置。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.
  7. 一种智能辅助诊疗方法，其特征在于，包括以下步骤：An intelligent assisted diagnosis and treatment method is characterized in that it includes the following steps:
    接收病历；Receiving medical records;
    根据疾病类型将接收到的所述病历进行分类处理，并将分类后的所述病历存储到相应的数据库；Classify the received medical records according to the type of disease, and store the classified medical records into a corresponding database;
    将所述病历与病历模板库中的病历模板进行匹配，获取匹配度大于预设阈值的病历模板，其中病历模板存储疾病类型及与疾病类型对应的病症信息；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;
    获取所匹配的疾病类型及与疾病类型对应的病症信息，利用人工智能卷积神经网络学习方法给出相应的历史诊断参考方案，并输出所匹配的疾病类型及与疾病类型对应的病症信息，以供医生参考。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.
  8. 根据权利要求7所述的智能辅助诊疗方法，其特征在于，所述病历包括电子病历和/或文档病历，在接收病历之后还包括：The method according to claim 7, wherein the medical record comprises an electronic medical record and / or a file medical record, and after receiving the medical record, the method further comprises:
    若接收的病历为文档病历，通过文字及图像分析按照人工智能深度学习方法将文本病历转换成规范化的电子病历；If the received medical records are document medical records, the text medical records are converted into standardized electronic medical records through text and image analysis according to artificial intelligence deep learning methods;
    若接收的病历为非规范化的电子病历及半规范化的电子病历，将非规范化的电子病历及半规范化的电子病历按照系统预设规则转换成规范化的电子病历。If 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.
  9. 根据权利要求7所述的智能辅助诊疗方法，其特征在于，在获取所匹配的疾病类型及与疾病类型对应的病症信息，利用人工智能卷积神经网络学习方法给出相应的历史诊断参考方案，并输出所匹配的疾病类型及与疾病类型对应的病症信息，以供医生参考的所述步骤之后还包括：The intelligent assisted diagnosis and treatment method according to claim 7, characterized in that, after obtaining the matched disease type and the disease information corresponding to the disease type, using an artificial intelligence convolutional neural network learning method to give a corresponding historical diagnosis reference scheme, And outputting the matched disease type and the disease information corresponding to the disease type, for the reference of the doctor, the steps further include:
    根据患者的疾病类型将多名医生对所匹配的疾病类型的治疗方案进行综合比对，得到包括对应病症信息及类似病症信息的诊疗方案传送至医生。According to the patient's disease type, 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.
  10. 根据权利要求7所述的智能辅助诊疗方法，其特征在于，还包括：The intelligent assisted diagnosis and treatment method according to claim 7, further comprising:
    在医生确定病人的疾病类型后，检查病历中是否还存在未被检查的项目或未被诊断的流程，在后续的治疗方案中及时反馈治疗方案的执行进度并给出治疗效果分析报告，提示医生是否在治疗过程中需要修改治疗方案进行二次辅助诊疗。After the doctor determines the type of patient's disease, check whether there are unchecked items or undiagnosed processes in the medical history, and timely feedback the progress of the implementation of the treatment plan in the subsequent treatment plan and give a treatment effect analysis report to remind the doctor Whether it is necessary to modify the treatment plan in the course of treatment for secondary auxiliary diagnosis and treatment.
  11. 根据权利要求7所述的智能辅助诊疗方法，其特征在于，还包括：The intelligent assisted diagnosis and treatment method according to claim 7, further comprising:
    接收医生的远程访问请求，并根据该访问请求与远程辅助系统建立通讯，以进行远程在线辅助。Receive a doctor's remote access request and establish communication with the remote assistance system according to the access request for remote online assistance.
  12. 根据权利要求7所述的智能辅助诊疗方法，其特征在于，还包括：The intelligent assisted diagnosis and treatment method according to claim 7, further comprising:
    提供门诊病历模板，接收医生的语音信号，对所采集的语音信号进行识别，将识别的语音信息转换为文本信息，并录入至门诊病历模板中的相应位置，生成病历。Provide 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.
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