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

Yogatheesan Varatharajah; Haotian Chen; Andrew Trotter; Ravishankar Iyer

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 journal › Conference article › peer-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 language	English (US)
Pages (from-to)	21-22
Number of pages	2
Journal	CEUR Workshop Proceedings
Volume	2684
State	Published - 2020
Externally published	Yes
Event	5th International Workshop on Health Recommender Systems, HealthRecSys 2020 - Virtual, Online Duration: Sep 26 2020 → …

Bibliographical note

Publisher Copyright:
© 2020 Copyright for the individual papers remains with the authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Keywords

COVID-19
Human-in-the-loop
Reinforcement learning
Staging

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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.",

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T1 - A dynamic human-in-the-loop recommender system for evidence-based clinical staging of COVID-19

AU - Varatharajah, Yogatheesan

AU - Chen, Haotian

AU - Trotter, Andrew

AU - Iyer, Ravishankar

PY - 2020

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N2 - 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.

AB - 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.

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KW - Human-in-the-loop

KW - Reinforcement learning

KW - Staging

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AN - SCOPUS:85093359044

SN - 1613-0073

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

Abstract

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Keywords

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