A dynamic human-in-the-loop recommender system for evidence-based clinical staging of COVID-19

Yogatheesan Varatharajah, Haotian Chen, Andrew Trotter, Ravishankar Iyer

Research output: Contribution to journalConference articlepeer-review

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 languageEnglish (US)
Pages (from-to)21-22
Number of pages2
JournalCEUR Workshop Proceedings
Volume2684
StatePublished - 2020
Event5th 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

  • Computer Science(all)

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