Development and validation of a deep learning model to predict the survival of patients in ICU.

ABSTRACT:

Background

Patients in the intensive care unit (ICU) are often in critical condition and have a high mortality rate. Accurately predicting the survival probability of ICU patients is beneficial to timely care and prioritizing medical resources to improve the overall patient population survival. Models developed by deep learning (DL) algorithms show good performance on many models. However, few DL algorithms have been validated in the dimension of survival time or compared with traditional algorithms.

Methods

Variables from the Early Warning Score, Sequential Organ Failure Assessment Score, Simplified Acute Physiology Score II, Acute Physiology and Chronic Health Evaluation (APACHE) II, and APACHE IV models were selected for model development. The Cox regression, random survi

SUBMITTER: Tang H 

PROVIDER: S-EPMC9382369 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature