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ICU hospital mortality prediction method based on deep learning

A technology of deep learning and prediction method, applied in the field of mortality prediction, can solve the problems of inaccurate prediction results of mortality of ICU patients, and achieve the effect of not easy to lose and fast convergence.

Active Publication Date: 2019-09-27
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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AI Technical Summary

Problems solved by technology

[0005] In view of the above-mentioned deficiencies in the prior art, a method for predicting in-hospital mortality in ICU based on deep learning provided by the present invention solves the problem of inaccurate prediction of in-hospital mortality in ICU patients

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  • ICU hospital mortality prediction method based on deep learning
  • ICU hospital mortality prediction method based on deep learning
  • ICU hospital mortality prediction method based on deep learning

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Embodiment Construction

[0046]The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0047] Such as figure 1 As shown, a deep learning-based ICU mortality prediction method includes the following steps:

[0048] S1. Collect the signs and indicators of patients within 48 hours after admission to the ICU;

[0049] Physical indicators include discrete variables including capillary refill rate, Glascow Coma Scale eye opening, Glascow Coma Scale motor response, Glascow Coma Scale verbal response, and Glascow Com...

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Abstract

The invention discloses an ICU hospital mortality prediction method based on deep learning. According to the method, the death in the ICU is predicted by using deep learning, dynamic sign change data of a patient is used, and important demographic characteristics such as age and disease type influencing the death rate are also added; three time gates used for controlling time increment change are added on the basis of a classic LSTM (multi-layer bidirectional and unidirectional long short-term memory model) network, so that the common problems of irregular sampling and data missing of clinical data are solved; an attention mechanism is introduced to fuse the hidden state at each moment, the convergence speed of the model is higher, and information contained in the initial stage is not prone to being lost.

Description

technical field [0001] The invention relates to the technical field of mortality prediction, in particular to a method for predicting mortality in ICU hospitals based on deep learning. Background technique [0002] Intensive Care Unit (ICU) admits and treats various critically ill patients, and usually requires daily monitoring of patients, such as heart rate, ECG, blood pressure, respiration, body temperature, etc., as well as special monitoring for different patients, so A large amount of monitoring data is generated every day, including physiological data collected by various medical and biosensors, as well as subjective evaluation results of medical staff. These time-series data can be used to classify patients, estimate hospital stays, and predict mortality. The most concerned part of the link is the prediction of in-hospital mortality, that is, to predict whether the patient will die during the ICU stay. ICU mortality prediction can help clinicians make auxiliary deci...

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Application Information

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IPC IPC(8): G16H50/20G16H50/30G06N3/04G06N3/08
CPCG16H50/20G16H50/30G06N3/08G06N3/048G06N3/044G06N3/045
Inventor 刘勇国刘朗李巧勤杨尚明曹晨任志扬傅翀
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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