Discrete-time survival analysis in the critically ill: a deep learning approach using heterogeneous data.

ABSTRACT: Prediction of survival for patients in intensive care units (ICUs) has been subject to intense research. However, no models exist that embrace the multiverse of data in ICUs. It is an open question whether deep learning methods using automated data integration with minimal pre-processing of mixed data domains such as free text, medical history and high-frequency data can provide discrete-time survival estimates for individual ICU patients. We trained a deep learning model on data from patients admitted to ten ICUs in the Capital Region of Denmark and the Region of Southern Denmark between 2011 and 2018. Inspired by natural language processing we mapped the electronic patient record data to an embedded representation and fed the data to a recurrent neural network with a multi-label output l

SUBMITTER: Thorsen-Meyer HC 

PROVIDER: S-EPMC9474816 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature