Name
Title | ||
|---|---|---|
A Dynamic Human-in-the-loop Recommender System for Evidence-based Clinical Staging of COVID-19. |
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\u0027s 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 © 2020 Copyright for the individual papers remains with the authors Use permitted under Creative Commons License Attribution 4 0 International (CC BY 4 0) |
Year | Venue | DocType |
|---|---|---|
2020 | HealthRecSys@RecSys | Conference |
Volume | Citations | PageRank |
2684 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Y. Varatharajah | 1 | 5 | 3.17 |
| Haotian Chen | 2 | 0 | 0.34 |
| Andrew Trotter | 3 | 0 | 0.34 |
| Ravishankar K. Iyer | 4 | 3489 | 504.32 |