Designing COVID-19 mortality predictions to advance clinical outcomes: Evidence from the Department of Veterans Affairs.
BMJ Health Care Inform
; 28(1)2021 Jun.
Article
in English
| MEDLINE | ID: covidwho-1263921
ABSTRACT
Using administrative data on all Veterans who enter Department of Veterans Affairs (VA) medical centres throughout the USA, this paper uses artificial intelligence (AI) to predict mortality rates for patients with COVID-19 between March and August 2020. First, using comprehensive data on over 10 000 Veterans' medical history, demographics and lab results, we estimate five AI models. Our XGBoost model performs the best, producing an area under the receive operator characteristics curve (AUROC) and area under the precision-recall curve of 0.87 and 0.41, respectively. We show how focusing on the performance of the AUROC alone can lead to unreliable models. Second, through a unique collaboration with the Washington D.C. VA medical centre, we develop a dashboard that incorporates these risk factors and the contributing sources of risk, which we deploy across local VA medical centres throughout the country. Our results provide a concrete example of how AI recommendations can be made explainable and practical for clinicians and their interactions with patients.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Veterans
/
Artificial Intelligence
/
Models, Statistical
/
COVID-19
Type of study:
Prognostic study
Limits:
Humans
Country/Region as subject:
North America
Language:
English
Year:
2021
Document Type:
Article
Affiliation country:
Bmjhci-2020-100312
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