Optimal Treatment Strategies for Critical Patients with Deep Reinforcement Learning
Article
Job, Simi, Tao, Xiaohui, Li, Lin, Xie, Haoran, Cai, Taotao, Yong, Jianming and Li, Qing. 2024. "Optimal Treatment Strategies for Critical Patients with Deep Reinforcement Learning
." ACM Transactions on Intelligent Systems and Technology. 15 (2), pp. 1-22. https://doi.org/10.1145/3643856
| Article Title | Optimal Treatment Strategies for Critical Patients with Deep Reinforcement Learning |
|---|---|
| ERA Journal ID | 200096 |
| Article Category | Article |
| Authors | Job, Simi, Tao, Xiaohui, Li, Lin, Xie, Haoran, Cai, Taotao, Yong, Jianming and Li, Qing |
| Journal Title | ACM Transactions on Intelligent Systems and Technology |
| Journal Citation | 15 (2), pp. 1-22 |
| Article Number | 36 |
| Number of Pages | 22 |
| Year | 2024 |
| Publisher | Association for Computing Machinery (ACM) |
| Place of Publication | United States |
| ISSN | 2157-6904 |
| 2157-6912 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1145/3643856 |
| Web Address (URL) | https://dl.acm.org/doi/10.1145/3643856 |
| Abstract | Personalized clinical decision support systems are increasingly being adopted due to the emergence of data-driven technologies, with this approach now gaining recognition in critical care. The task of incorporating diverse patient conditions and treatment procedures into critical care decision-making can be challenging due to the heterogeneous nature of medical data. Advances in Artificial Intelligence (AI), particularly Reinforcement Learning (RL) techniques, enables the development of personalized treatment strategies for severe illnesses by using a learning agent to recommend optimal policies. In this study, we propose a Deep Reinforcement Learning (DRL) model with a tailored reward function and an LSTM-GRU-derived state representation to formulate optimal treatment policies for vasopressor administration in stabilizing patient physiological states in critical care settings. Using an ICU dataset and the Medical Information Mart for Intensive Care (MIMIC-III) dataset, we focus on patients with Acute Respiratory Distress Syndrome (ARDS) that has led to Sepsis, to derive optimal policies that can prioritize patient recovery over patient survival. Both the DDQN (RepDRL-DDQN) and Dueling DDQN (RepDRL-DDDQN) versions of the DRL model surpass the baseline performance, with the proposed model’s learning agent achieving an optimal learning process across our performance measuring schemes. The robust state representation served as the foundation for enhancing the model’s performance, ultimately providing an optimal treatment policy focused on rapid patient recovery. |
| Keywords | DDDQN; Deep reinforcement learning; Q-learning; Q netwoks; DQN; DDQN; treatment strategies |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 461103. Deep learning |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | School of Mathematics, Physics and Computing |
| Wuhan University of Technology, China | |
| Lingnan University of Hong Kong, China | |
| School of Business | |
| Hong Kong Polytechnic University, China |
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https://research.usq.edu.au/item/z8500/optimal-treatment-strategies-for-critical-patients-with-deep-reinforcement-learning
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