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Expert system and diagnosis and treatment system for patient recovery

An expert system and patient technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as no one cares, diseases are difficult to obtain effective and correct diagnosis and treatment, and large hospitals are overcrowded. , to avoid crowded queuing environment, be beneficial to recovery as soon as possible, and prevent mutual infection.

Inactive Publication Date: 2014-12-17
SUZHOU INST OF BIOMEDICAL ENG & TECH CHINESE ACADEMY OF SCI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At the same time, due to the uneven distribution of medical resources, it is difficult for residents in underdeveloped areas and small towns to receive effective and correct diagnosis and treatment of diseases
[0003] As mentioned above, the problem of difficulty in seeing a doctor has arisen. Large hospitals queue up for registration, wait in line for doctors to see a doctor, wait in line for various examinations, and queue up for payment of fees and medicines. At the same time, a problem arises: small hospitals such as community hospitals cannot People care about it, resulting in low utilization rate of diagnostic and treatment equipment, or even idle
[0006] (1) Large hospitals are overcrowded and medical staff are busy;
[0007] (2) Part of the equipment in the small hospital is idle;
[0009] (4) Patients lack the guidance of doctors during the treatment process

Method used

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  • Expert system and diagnosis and treatment system for patient recovery
  • Expert system and diagnosis and treatment system for patient recovery
  • Expert system and diagnosis and treatment system for patient recovery

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0052] see figure 1 As shown, an expert system for patient rehabilitation, the system provides remote treatment to patients through physician network diagnosis, and is characterized in that: the expert system includes a database, a server, a storage unit and peripherals, and the database is in accordance with Resource types are divided into genetic disease data and non-genetic disease data, servers include master service modules and slave service modules, and peripherals include network ports, printing devices, scanning devices, input devices, display devices, and external storage devices.

[0053] Further, the database is a network database, based on the background database to realize interconnected management through network backup, the main service module includes a foreground module, an analysis module and a prediction module, and the foreground module is used to complete data download, query and data upload The analysis module is used to retrieve and store the data on th...

Embodiment 2

[0063] Compared with Example 1, Example 2 further clarifies the establishment method of the expert system. The specific establishment method can be carried out according to the following steps:

[0064] (1) Investigation of medical resources and establishment of database;

[0065] The survey content includes doctor information, clinic information, and diagnostic equipment information; doctor information includes name, gender, specialty, qualifications, diseases that are good at diagnosis and treatment, working years, and affiliated clinics; clinic information includes name, grade, geographical location, and diagnostic equipment. Summary; medical equipment information includes equipment name and model, diagnostic accuracy, new and old conditions, frequency of use, affiliated clinics, number of operators, and proficiency; associate doctor information, clinic information, and diagnostic equipment information through a database; the medical resources The integrated system can be ...

Embodiment 3

[0069] Compared with embodiment 2, embodiment 3 further clarifies the prediction model of the prediction module of the expert system. The specific steps are as follows:

[0070] Firstly, the patient data is extracted from the doctor’s usage module, and the Bayesian model is selected as the prediction model. The methods for learning the Bayesian network structure include Markov chain Monte Carlo local search method or simulated annealing method or method based on ant colony optimization. Assuming that the variable set X={X1, X2,...,Xn}, the Bayesian network structure S encodes the set of conditional independence assertions related to the variables in X, which is represented by a directed acyclic graph, and then from the patient The parameters of the data-learned Bayesian network, which form part of the conditionally probabilistic defined Bayesian network, can be estimated from patient data using the Expectation-Maximization (EM) method, which is useful in addressing two problem...

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Abstract

The invention discloses an expert system, and a diagnosis and treatment system comprising the system. The system comprises a main service module, a doctor module, a diagnosis point module, a patient module, a storage unit, a network port, a printing device, a scanning device, an input device, a displaying device and an external storing device; a predicating module in the main service module is used for uploading the data from the doctor module to a background database through a foreground module; a doctor analyzes and refers to the data through the doctor module and then creates a clinical report. According to the expert system and the diagnosis and treatment system for the patient recovery, the predicating module produces risk score and recovery strategy through a predicating model, and therefore, the credibility of the diagnosis and treatment and system is improved.

Description

technical field [0001] The invention relates to the field of disease diagnosis and treatment, in particular to an integration system and application method of disease diagnosis and treatment resources. Background technique [0002] Due to the uneven distribution of medical resources, good diagnosis and treatment equipment, excellent doctors and nursing care are concentrated in a few large hospitals in major cities. Residents in the county want to go to municipal hospitals for medical treatment, residents in cities want to go to provincial hospitals for medical treatment, and even want to go to Shanghai, Beijing and other cities with the most abundant medical resources for medical treatment. In many provincial capital cities, as well as other large and medium-sized cities, the problem of difficulty in seeing a doctor has become increasingly prominent. At the same time, due to the uneven distribution of medical resources, it is difficult for residents in underdeveloped areas ...

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor 黄瑞唐玉国袁艳明
Owner SUZHOU INST OF BIOMEDICAL ENG & TECH CHINESE ACADEMY OF SCI
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