Reinforcement Learning for Heart Failure Treatment Optimization in the Intensive Care Unit
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Date
2024
Publication Type
Conference Paper
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yes
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Abstract
Heart failure (HF) is a major public health issue. Despite improvements in treatment, mortality rates among HF patients remain high, especially for those in the intensive care unit (ICU) who experience the highest in-hospital mortality rates.Clinical guidelines for the treatment of HF provide general recommendations, that however often lack strong evidence derived from randomized controlled trials (RCTs). Furthermore, they can only provide general guidance and fail to determine personalized strategies.Previous literature has shown that reinforcement learning (RL) is effective in determining optimal treatment recommendations in critical care settings. In this study, we used RL to address uncertainty in the administration of vasopressors and diuretics while considering individual patient characteristics. We utilized data from the MIMIC-IV database to demonstrate the potential of RL in improving treatment strategies for HF.The study indicates that RL achieved a significant mortality reduction of ≈ 20%. However, further research is necessary due to the lack of external validation and limitations in policy evaluation.Clinical relevance—This study adds to the growing body of evidence that demonstrates the potential of RL in identifying optimal treatment strategies in critical care settings. Specifically, the policy estimated by RL reduced mortality rates of HF patients in the ICU by ≈20% compared to the observed clinician policy.
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published
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Book title
2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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Volume
Pages / Article No.
10781564
Publisher
IEEE
Event
46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2024)
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Methods
Software
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Subject
Decision support systems; Uncertainty; Databases; MIMICs; Reinforcement learning; Cardiovascular diseases; Public healthcare; Engineering in medicine and biology; Optimization; Guidelines; Reinforcement learning; Intensive care; Intensive Care Unit; Heart failure
Organisational unit
09568 - Rätsch, Gunnar / Rätsch, Gunnar