Deep Reinforcement Learning for Efficient and Fair Allocation of Healthcare Resources

Presentation Time: 08:45 AM - 09:00 AM

Abstract Keywords: Deep Learning, Fairness and elimination of bias, Data Mining
Primary Track: Applications
Programmatic Theme: Clinical Informatics

During health crises like the COVID-19 pandemic, scarce resources like ventilators necessitate rationing. Current allocation protocols vary widely, lacking a standardized approach. We explore reinforcement learning for critical care resource allocation optimization. Our transformer-based deep Q-network integrates patient disease progression and interactions for fairer, more effective resource distribution. Experiments show reduced excess deaths and improved equity compared to existing methods across different levels of ventilator shortage.

Speaker(s):
Yikuan Li, M.Sci
Northwestern University

Author(s):
Chengsheng Mao, Ph.D - Northwestern University - Feinberg School of Medicine; Hanyin Wang, PhD - Northwestern University; Kaixuan Huang, BS - Princeton University; Zheng Yu, PhD - Princeton University; Mengdi Wang, PhD - Princeton University; Yuan Luo, PhD - Northwestern University;