Work

Deep Attention Q-Network for Personalized Treatment Recommendation

Year: 2023

Type: conference-paper

Abstract: Tailoring treatment for severely ill patients is crucial yet challenging to achieve optimal healthcare outcomes. Recent advances in reinforcement learning offer promising personalized treatment recommendations. However, they often rely solely on a patient's current physiological state, which may not accurately represent the true health status of the patient. This limitation hampers policy learning and evaluation, undermining the effectiveness of the treatment. In this study, we propose Deep Attention Q-Network for personalized treatment recommendation, leveraging the Transformer architecture within a deep reinforcement learning framework to efficiently integrate historical observations of patients. We evaluated our proposed method on two real-world datasets: sepsis and acute hypotension patients, demonstrating its superiority over state-of-the-art methods. The source code for our model is available at https://github.com/stevenmsm/RL-ICU-DAQN.

Source: IEEE ... International Conference on Data Mining workshops

DOI: 10.1109/icdmw60847.2023.00048

Authors: Simin Ma, Junghwan Lee, Nicoleta Serban, Shihao Yang

Institution: Georgia Institute of Technology

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases