Artificial intelligence auxiliary diagnosis and treatment system and construction method thereof, equipment and storage medium

A diagnosis and treatment system, artificial intelligence technology, applied in computer-aided medical procedures, medical automatic diagnosis, medical data mining and other directions, can solve the problems of complex operation, low use efficiency, insufficient intelligence, etc., to achieve a comprehensive underlying knowledge map, The effect of high intelligence and high efficiency

Inactive Publication Date: 2019-02-15
长沙瀚云信息科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] Through the analysis of the above content, it can be known that the existing medical diagnosis system has the following technical problems: 1) It cannot do artificial intelligence-assisted diagnosis and treatment well, but only provides doctors with reference to the inspection items, inspection items, surgical items, and therapeutic drugs of certain types of diseases etc.; the underlying knowledge graph is not comprehensive and authoritative enough; 2) It cannot reduce the workload of doctors; 3) It cannot provide clinical diagnosis and treatment paths

Method used

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  • Artificial intelligence auxiliary diagnosis and treatment system and construction method thereof, equipment and storage medium
  • Artificial intelligence auxiliary diagnosis and treatment system and construction method thereof, equipment and storage medium
  • Artificial intelligence auxiliary diagnosis and treatment system and construction method thereof, equipment and storage medium

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Embodiment 1

[0044] An artificial intelligence-assisted diagnosis and treatment system includes a disease knowledge map module, a disease diagnosis model module, and a machine learning decision-making module; wherein, the disease knowledge map module is used to collect various resources about diseases and form a disease knowledge base to for inquiry and reading;

[0045] The disease diagnosis model module is used to receive patient information, store the information in the system database, and input it to the machine learning decision-making module, and use the machine learning decision-making module to extract index data from the disease knowledge map module for comparative analysis, and push out the patient An information set, indicating the examinations that patients need to implement and the related drug treatment plan set for doctors to choose;

[0046] The machine learning decision-making module is used to receive patient information and doctor practice information, read disease know...

Embodiment 2

[0049] An artificial intelligence-assisted diagnosis and treatment system in this embodiment is further improved on the basis of Embodiment 1. The index data extracted from the disease knowledge map module for comparative analysis is: to determine the possibility of a disease through the connection of the disease knowledge map It can display the previous treatment methods, medication data and diagnosis and treatment expenses related information for this disease, so as to assist clinicians, outpatient medical treatment and other medical workers to be more prepared to grasp the whole process of disease treatment.

[0050] The decision is: recommend the suspected rate of suspected disease that does not exceed or approach the preset threshold; the comparative analysis includes comparing the information of previous visits for patients who have visited the hospital many times to help doctors more quickly and accurately Make a disease diagnosis. The query refers to the name of a cert...

Embodiment 3

[0052] A construction method of an artificial intelligence-assisted diagnosis and treatment system of the present embodiment, according to an artificial intelligence-assisted diagnosis and treatment system of embodiment 1 or 2, the steps are:

[0053] A. Construct a disease knowledge map: comprehensively collect various resources about diseases to form a fusion disease knowledge collection and disease top-level ontology;

[0054] B. Build a disease diagnosis model: receive patient information, store the information in the system database, and make machine learning decisions, extract index data from the disease knowledge map module through machine learning decisions for comparative analysis, push out patient information sets, and prompt out The examinations that patients need to implement and the program set of related drug treatments are available for doctors to choose.

[0055] The method described in this embodiment can be run and used on mobile terminals such as PCs, mobile...

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Abstract

The invention discloses an artificial intelligence auxiliary diagnosis and treatment system and a construction method thereof, equipment and a storage medium and belongs to the technical field of lifehealth. The system includes a disease knowledge map module, a disease diagnosis model module and a machine learning decision module, wherein the disease knowledge map module is configured to collectvarious resources about diseases and form a disease knowledge database for querying and reading, the disease diagnosis model module is configured to receive the patient information, store the information in the system database, input the information to the machine learning decision module, extract indicator data from the disease knowledge map module through the machine learning decision module toperform comparative analysis, push a patient information set and suggest an examination which a patient needs to take and a scheme set of related drug treatment for choose of doctors. The system is advantaged in that the underlying knowledge map is comprehensive and authoritative, interaction operation is simple, use efficiency is high, on the human-computer interaction level, the intelligence degree is high.

Description

technical field [0001] The present invention relates to the technical field of life and health, and in particular to an artificial intelligence-assisted diagnosis and treatment system and its construction method, equipment and storage medium. Background technique [0002] According to the World Health Organization survey, a large number of patients worldwide die from medical errors rather than the disease itself. In 1999, IOM (National Institute of Medicine) published an epoch-making report "Toerris Human" (people make mistakes), the report shows: First, the number of medical errors is alarming, and medical errors are among the top ten causes of death Ranked fifth; second, most of the medical errors come from human factors, which can be avoided by computer systems. In 2013, an analysis published in the American journal Patient safety & quality healthcare estimated that 210,000 to 440,000 people died of medical accidents in the United States every year, making it the third l...

Claims

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Application Information

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IPC IPC(8): G16H50/20G16H50/50G16H50/70
CPCG16H50/20G16H50/50G16H50/70
Inventor 蒋小云
Owner 长沙瀚云信息科技有限公司
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