Combining patient visual timelines with deep learning to predict mortality.

ABSTRACT:

Background

Deep learning algorithms have achieved human-equivalent performance in image recognition. However, the majority of clinical data within electronic health records is inherently in a non-image format. Therefore, creating visual representations of clinical data could facilitate using cutting-edge deep learning models for predicting outcomes such as in-hospital mortality, while enabling clinician interpretability. The objective of this study was to develop a framework that first transforms longitudinal patient data into visual timelines and then utilizes deep learning to predict in-hospital mortality.

Methods and findings

All adult consecutive patient admissions from 2008-2016 at a tertiary care center were included in this retrospective study. Two-dimensional visual

SUBMITTER: Mayampurath A 

PROVIDER: S-EPMC6668841 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature