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VTE risk automatic evaluation system based on deep learning

A deep learning, automatic evaluation technology, applied in the field of VTE risk automatic evaluation system, can solve the problems of heavy workload, time-consuming, lack of nursing staff, etc., to reduce workload, improve accuracy, and ensure the effect of accuracy

Active Publication Date: 2021-01-22
SHAN DONG MSUN HEALTH TECH GRP CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The inventors of the present disclosure have found that the prior art has the following defects: (1) most of the current VTE assessments are done manually by nursing staff, which takes a lot of work and takes a lot of time
Moreover, many risk items involved in the evaluation process require professional medical knowledge for their judgment, and nursing staff generally lack relevant medical knowledge, resulting in inaccurate judgment results; (2) The current VTE evaluation system cannot comprehensively analyze all aspects of patients in the hospital information system automatically and accurately complete the VTE risk assessment, which has a negative impact on the prevention of VTE in patients

Method used

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  • VTE risk automatic evaluation system based on deep learning
  • VTE risk automatic evaluation system based on deep learning
  • VTE risk automatic evaluation system based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0060] like figure 1 As shown, Embodiment 1 of the present disclosure provides an automatic VTE risk assessment system based on deep learning. Based on artificial intelligence technology, it automatically analyzes various aspects of the patient's data in the hospital information system, and automatically fills in several types of VTE commonly seen in the medical field. Risk assessment forms (eg, Caprini, Rogers, Pauda, ​​Wells, etc. assessment forms) to complete the VTE risk assessment.

[0061] Specifically, it includes an unstructured risk index comprehensive analysis module (ie the first analysis module), a structured risk index single analysis module (ie the second analysis module), an unstructured risk index judgment module (ie the first judgment module), Structured risk index judgment module (ie the second judgment module) and risk item judgment module;

[0062] In the "unstructured risk index comprehensive analysis module", based on the patient's information related to...

Embodiment 2

[0162] Embodiment 2 of the present disclosure provides a computer-readable storage medium on which a program is stored, and when the program is executed by a processor, the following steps are implemented:

[0163] According to the preset VTE knowledge map and the first deep learning model, based on the patient's information related to all risk items, obtain the probability that all unstructured risk indicators of the patient are selected;

[0164] According to the preset VTE knowledge map and the second deep learning model, based on the patient's information related to an unstructured risk index, obtain the probability that a patient's unstructured risk index is selected;

[0165] Based on the probability that all unstructured risk indicators of the patient are selected and the probability that a certain unstructured risk indicator of the patient is selected, determine whether the unstructured risk indicator is selected;

[0166] According to the preset VTE knowledge map, det...

Embodiment 3

[0170] Embodiment 3 of the present disclosure provides an electronic device, including a memory, a processor, and a program stored in the memory and running on the processor, where the processor implements the following steps when executing the program:

[0171] According to the preset VTE knowledge map and the first deep learning model, based on the patient's information related to all risk items, obtain the probability that all unstructured risk indicators of the patient are selected;

[0172] According to the preset VTE knowledge map and the second deep learning model, based on the patient's information related to an unstructured risk index, obtain the probability that a patient's unstructured risk index is selected;

[0173] Based on the probability that all unstructured risk indicators of the patient are selected and the probability that a certain unstructured risk indicator of the patient is selected, determine whether the unstructured risk indicator is selected;

[0174...

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Abstract

The invention provides a VTE risk automatic evaluation system based on deep learning, and the system comprises a first analysis module which obtains the probability that all unstructured risk indexesof a patient are selected according to a preset VTE knowledge graph based on information, related to all risk items, of the patient, a second analysis module used for obtaining the probability that acertain unstructured risk index of the patient is selected on the basis of the information, related to the certain unstructured risk index, of the patient, a first judgment moduleused for judging whether the unstructured risk index is selected or not based on results of the first analysis module and the second analysis module, a second judgment module used for judging whether the structured risk index is selected or not, and a risk project evaluation module used for judging that the risk project is at risk based on the judgment results of the first judgment module and the second judgment module when any risk index is selected; according to the invention, the workload of nursing personnel is greatly reduced, and the accuracy of VTE risk assessment is improved.

Description

technical field [0001] The present disclosure relates to the technical field of health risk assessment, in particular to a deep learning-based VTE risk automatic assessment system. Background technique [0002] The statements in this section merely provide background related to the present disclosure and do not necessarily constitute prior art. [0003] Venous Thromboembolism (VTE) is a disease that seriously threatens the lives of hospitalized patients. In order to prevent the occurrence of VTE as much as possible, hospitalized patients need to perform multiple VTE risk assessments during hospitalization, and medical staff take corresponding preventive measures according to the results of the patient's risk assessment. Therefore, it is of great significance to accurately assess the risk of VTE in patients. At present, the method of VTE risk assessment is that nurses ask patients according to various authoritative assessment forms (for example, Caprini, Rogers, Pauda, ​​We...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/30G16H50/70
CPCG16H50/30G16H50/70
Inventor 孙钊吴军高希余刘小梅冯德杰段惠斌
Owner SHAN DONG MSUN HEALTH TECH GRP CO LTD