A new study by Weill Cornell Medicine and Rockefeller University researchers finds that Reinforcement Learning (RL), an artificial intelligence approach, holds promise for guiding physicians in designing sequential treatment strategies for better patient outcomes. However, significant improvements are needed before RL can be applied in clinical settings due to its high data requirements. The research introduces 'Episodes of Care' (EpiCare), the first RL benchmark for healthcare, aiming to accelerate progress in this field.
Weill Cornell MedicineDec 17 2024 Reinforcement Learning , an artificial intelligence approach, has the potential to guide physicians in designing sequential treatment strategies for better patient outcomes but requires significant improvements before it can be applied in clinical settings, finds a new study by Weill Cornell Medicine and Rockefeller University researchers.
"Benchmarks have driven improvement across machine learning applications including computer vision, natural language processing, speech recognition and self-driving cars. We hope they will now push RL progress in healthcare," said Dr. Logan Grosenick, assistant professor of neuroscience in psychiatry, who led the research.
"Our findings indicate that current state-of-the-art OPE methods cannot be trusted to accurately predict reinforcement learning performance in longitudinal health care scenarios," said first author Dr. Mason Hargrave, research fellow at The Rockefeller University.
Related StoriesBrain networks are typically represented as graphs where brain regions propagate information to other brain regions along "edges" that connect and represent the strength between them. This is also true of gene and protein networks, human and animal behavioral data and of the geometry of chemical compounds like drugs. By analyzing such graphs directly, we can more accurately model dependencies and patterns between both local and more distant connections.
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