Axes of Prognosis: Identifying Subtypes of COVID-19 Outcomes
AMIA ... Annual Symposium Proceedings/AMIA Symposium
; 2021:1198-1207, 2021.
Article
in English
| MEDLINE | ID: covidwho-1749821
ABSTRACT
COVID-19 is a disease with vast impact, yet much remains unclear about patient outcomes. Most approaches to risk prediction of COVID-19 focus on binary or tertiary severity outcomes, despite the heterogeneity of the disease. In this work, we identify heterogeneous subtypes of COVID-19 outcomes by considering 'axes' of prognosis. We propose two innovative clustering approaches - 'Layered Axes' and 'Prognosis Space' - to apply on patients' outcome data. We then show how these clusters can help predict a patient's deterioration pathway on their hospital admission, using random forest classification. We illustrate this methodology on a cohort from Wuhan in early 2020. We discover interesting subgroups of poor prognosis, particularly within respiratory patients, and predict respiratory subgroup membership with high accuracy. This work could assist clinicians in identifying appropriate treatments at patients' hospital admission. Moreover, our method could be used to explore subtypes of 'long COVID' and other diseases with heterogeneous outcomes.
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Collection:
Databases of international organizations
Database:
MEDLINE
Type of study:
Prognostic study
Language:
English
Journal:
AMIA Symposium
Year:
2021
Document Type:
Article
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