Deep Learning Methods to Predict Mortality in COVID-19 Patients: A Rapid Scoping Review

Abstract

The ongoing COVID-19 pandemic has become the most impactful pandemic of the past century. The SARS-CoV-2 virus has spread rapidly across the globe affecting and straining global health systems. More than 2 million people have died from COVID-19 (as of 30 January 2021). To lessen the pandemic’s impact, advanced methods such as Artificial Intelligence models are proposed to predict mortality, morbidity, disease severity, and other outcomes and sequelae. We performed a rapid scoping literature review to identify the deep learning techniques that have been applied to predict hospital mortality in COVID-19 patients. Our review findings provide insights on the important deep learning models, data types, and features that have been reported in the literature. These summary findings will help scientists build reliable and accurate models for better intervention strategies for predicting mortality in current and future pandemic situations.

All Patient Refined-Diagnosis Related Groups’ (APR-DRGs) Severity of Illness and Risk of Mortality as predictors of in-hospital mortality

“…Actually, there is increasing research on using artificial intelligence for clinical coding automation, namely with machine learning and natural language processing techniques [40][41][42]. Moreover, additional clinical information not usually coded into such administrative databases is being more and more combined with artificial intelligence techniques for predicting mortality [43][44][45][46].…”

Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review

“…Considering that none of the available reviews on the use of AI in the predictive modeling of COVID-19-related outcomes have performed a bias/applicability assessment of AI models used to predict such outcomes (7)(8)(9)(10)(11)(12)(13)(14) (Table 1), we conducted a systematic screening aiming at filling this knowledge gap. According to the definitions of the tool used for assessing the risk of bias in this systematic review (i.e., Bias analysis using the Prediction model Risk Of Bias ASsessment Tool, PROBAST), bias was defined to occur when shortcomings in the study design, conduct, or analysis lead to systematically distorted estimates of model predictive performance.…”