Fusion of imaging and non-imaging data for disease trajectory prediction for coronavirus disease 2019 patients.

ABSTRACT:

Purpose

Our study investigates whether graph-based fusion of imaging data with non-imaging electronic health records (EHR) data can improve the prediction of the disease trajectories for patients with coronavirus disease 2019 (COVID-19) beyond the prediction performance of only imaging or non-imaging EHR data.

Approach

We present a fusion framework for fine-grained clinical outcome prediction [discharge, intensive care unit (ICU) admission, or death] that fuses imaging and non-imaging information using a similarity-based graph structure. Node features are represented by image embedding, and edges are encoded with clinical or demographic similarity.

Results

Experiments on data collected from the Emory Healthcare Network indicate that our fusion modeling scheme performs

SUBMITTER: Tariq A 

PROVIDER: S-EPMC10306115 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature