Interpretable deep learning for the prediction of ICU admission likelihood and mortality of COVID-19 patients
Source of Publication
PeerJ Computer Science
The global healthcare system is being overburdened by an increasing number of COVID-19 patients. Physicians are having difficulty allocating resources and focusing their attention on high-risk patients, partly due to the difficulty in identifying high-risk patients early. COVID-19 hospitalizations require specialized treatment capabilities and can cause a burden on healthcare resources. Estimating future hospitalization of COVID-19 patients is, therefore, crucial to saving lives. In this paper, an interpretable deep learning model is developed to predict intensive care unit (ICU) admission and mortality of COVID-19 patients. The study comprised of patients from the Stony Brook University Hospital, with patient information such as demographics, comorbidities, symptoms, vital signs, and laboratory tests recorded. The top three predictors of ICU admission were ferritin, diarrhoea, and alamine aminotransferase, and the top predictors for mortality were COPD, ferritin, and myalgia. The proposed model predicted ICU admission with an AUC score of 88.3% and predicted mortality with an AUC score of 96.3%. The proposed model was evaluated against existing model in the literature which achieved an AUC of 72.8% in predicting ICU admission and achieved an AUC of 84.4% in predicting mortality. It can clearly be seen that the model proposed in this paper shows superiority over existing models. The proposed model has the potential to provide tools to frontline doctors to help classify patients in time-bound and resource-limited scenarios.
Interpretable deep learning, Prediction of ICU admission, Prediction of mortality, COVID-19
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Nazir, Amril and Ampadu, Hyacinth Kwadwo, "Interpretable deep learning for the prediction of ICU admission likelihood and mortality of COVID-19 patients" (2022). All Works. 4943.
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Open Access Type
Gold: This publication is openly available in an open access journal/series