Abstract
In this position paper, we discuss the potential use of a reinforcement learning (RL)-based human-in-the-loop recommender system to support clinical management of COVID-19. COVID-19 is a disease of extraordinary complexity that even the most experienced clinicians are struggling to understand. There is an urgent need for an evidence-based model for predicting the severity of the COVID-19 disease and its complications that can guide individual clinical management decisions. Such a model will utilize a diverse set of information to determine a patient's disease severity and associated risk of complications. An immediate application would be a clinical protocol tailored for COVID-19 patient care; this is a critical need both today and for future studies of potential treatments.
Original language | English (US) |
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Pages (from-to) | 21-22 |
Number of pages | 2 |
Journal | CEUR Workshop Proceedings |
Volume | 2684 |
State | Published - 2020 |
Event | 5th International Workshop on Health Recommender Systems, HealthRecSys 2020 - Virtual, Online Duration: Sep 26 2020 → … |
Keywords
- COVID-19
- Human-in-the-loop
- Reinforcement learning
- Staging
ASJC Scopus subject areas
- General Computer Science