Khadem, H. orcid.org/0000-0002-6878-875X, Nemat, H., Eissa, M.R. orcid.org/0000-0002-5584-5815 et al. (2 more authors) (2022) COVID-19 mortality risk assessments for individuals with and without diabetes mellitus : machine learning models integrated with interpretation framework. Computers in Biology and Medicine, 144. 105361. ISSN 0010-4825
Abstract
This research develops machine learning models equipped with interpretation modules for mortality risk prediction and stratification in cohorts of hospitalised coronavirus disease-2019 (COVID-19) patients with and without diabetes mellitus (DM). To this end, routinely collected clinical data from 156 COVID-19 patients with DM and 349 COVID-19 patients without DM were scrutinised. First, a random forest classifier forecasted in-hospital COVID-19 fatality utilising admission data for each cohort. For the DM cohort, the model predicted mortality risk with the accuracy of 82%, area under the receiver operating characteristic curve (AUC) of 80%, sensitivity of 80%, and specificity of 56%. For the non-DM cohort, the achieved accuracy, AUC, sensitivity, and specificity were 80%, 84%, 91%, and 56%, respectively. The models were then interpreted using SHapley Additive exPlanations (SHAP), which explained predictors’ global and local influences on model outputs. Finally, the k-means algorithm was applied to cluster patients on their SHAP values. The algorithm demarcated patients into three clusters. Average mortality rates within the generated clusters were 8%, 20%, and 76% for the DM cohort, 2.7%, 28%, and 41.9% for the non-DM cohort, providing a functional method of risk stratification.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2022 Elsevier Ltd. This is an author produced version of a paper subsequently published in Computers in Biology and Medicine. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Machine learning; COVID-19; Diabetes mellitus; Risk assessment; Model interpretation |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) The University of Sheffield > Sheffield Teaching Hospitals |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 07 Mar 2022 08:17 |
Last Modified: | 02 Mar 2023 01:13 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.compbiomed.2022.105361 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184443 |