tetano
Editor, Senior Moderator
Comput Biol Med
. 2022 Mar 2;144:105361.
doi: 10.1016/j.compbiomed.2022.105361. Online ahead of print.
COVID-19 mortality risk assessments for individuals with and without diabetes mellitus: Machine learning models integrated with interpretation framework
Heydar Khadem[SUP] 1 [/SUP], Hoda Nemat[SUP] 2 [/SUP], Mohammad R Eissa[SUP] 3 [/SUP], Jackie Elliott[SUP] 4 [/SUP], Mohammed Benaissa[SUP] 5 [/SUP]
Affiliations
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.
Keywords: COVID-19; Diabetes mellitus; Machine learning; Model interpretation; Risk assessment.
. 2022 Mar 2;144:105361.
doi: 10.1016/j.compbiomed.2022.105361. Online ahead of print.
COVID-19 mortality risk assessments for individuals with and without diabetes mellitus: Machine learning models integrated with interpretation framework
Heydar Khadem[SUP] 1 [/SUP], Hoda Nemat[SUP] 2 [/SUP], Mohammad R Eissa[SUP] 3 [/SUP], Jackie Elliott[SUP] 4 [/SUP], Mohammed Benaissa[SUP] 5 [/SUP]
Affiliations
- PMID: 35255295
- DOI: 10.1016/j.compbiomed.2022.105361
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.
Keywords: COVID-19; Diabetes mellitus; Machine learning; Model interpretation; Risk assessment.