Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study.

ABSTRACT: Predicting clinical deterioration in COVID-19 patients remains a challenging task in the Emergency Department (ED). To address this aim, we developed an artificial neural network using textual (e.g. patient history) and tabular (e.g. laboratory values) data from ED electronic medical reports. The predicted outcomes were 30-day mortality and ICU admission. We included consecutive patients from Humanitas Research Hospital and San Raffaele Hospital in the Milan area between February 20 and May 5, 2020. We included 1296 COVID-19 patients. Textual predictors consisted of patient history, physical exam, and radiological reports. Tabular predictors included age, creatinine, C-reactive protein, hemoglobin, and platelet count. TensorFlow tabular-textual model performance indices were compared to th

SUBMITTER: Dipaola F 

PROVIDER: S-EPMC10322913 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature