Time window cutting-based health risk key event detection method and system

Abstract

The invention discloses a time window cutting-based health risk key event detection method and system. The method comprises the following steps: S100, inputting an electronic medical record of a patient into a trained deep learning model to obtain important indexes and death risk indexes of physiological indexes of a patient every time the patient sees a doctor; S200, dividing targets of detectionof health risk key events of the patient into four types according to time windows, detecting the four types on the basis of the obtained important index and the death risk index of each physiological index to screen out the physiological indexes of the health risk key events which need to be proposed; and S300, based on a visual interface, displaying the detection process and detection results of the health risk key events of the patient. The method and system can mine more valuable information with reference significance from data of the electronic medical record of the patient in the hospital to help the doctor make diagnosis.

Description

technical field [0001] The invention relates to the field of health risk detection, in particular to a method and system for detecting key health risk events based on time window cutting. Background technique [0002] For end-stage patients with chronic diseases and critically ill patients, assessing their risk of death will help medical staff to take targeted treatment measures as soon as possible to improve the survival probability of patients. However, the assessment of mortality risk is very complicated. It needs to comprehensively analyze multiple physiological indicators of the patient, consider static information such as the patient's age, gender, and past medical history, and combine a large amount of medical knowledge to make the final judgment. Only those with rich medical knowledge and rich clinical experience Doctors can get more accurate evaluation results only after investing a lot of time and energy. However, in clinical practice, medical institutions often h...

AI Technical Summary

Problems solved by technology

However, the assessment of mortality risk is very complicated. It needs to comprehensively analyze multiple physiological indicators of the patient, consider static information such as the patient's age, gender, and past medical history, and combine a large amount of medical knowledge to make the final judgment. Only those with rich medical knowledge and clinical experience Doctors can get more accurate assessment results only after investing a lot of time and energy

However, in clinical practice, medical institutions often have poor evaluation results due to the lack of sufficient expert resources

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