Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space

• URL: http://arxiv.org/abs/2206.11190v1
• Date: Wed, 22 Jun 2022 16:17:21 GMT
• Title: Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space
• Authors: Zeyu Wang, Huiying Zhao, Peng Ren, Yuxi Zhou, Ming Sheng
• Abstract summary: We propose a new medical decision model based on historical data to help clinicians recommend the best reference option for real-time treatment. Our model combines offline reinforcement learning with deep reinforcement learning to address the problem that traditional reinforcement learning in healthcare cannot interact with the environment.
• Score: 4.031538204818658
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sepsis is a leading cause of death in the ICU. It is a disease requiring complex interventions in a short period of time, but its optimal treatment strategy remains uncertain. Evidence suggests that the practices of currently used treatment strategies are problematic and may cause harm to patients. To address this decision problem, we propose a new medical decision model based on historical data to help clinicians recommend the best reference option for real-time treatment. Our model combines offline reinforcement learning with deep reinforcement learning to address the problem that traditional reinforcement learning in healthcare cannot interact with the environment, enabling our model to make decisions in a continuous state-action space. We demonstrate that, on average, the treatments recommended by the model are more valuable and reliable than those recommended by clinicians. In a large validation dataset, we found that patients whose actual doses from clinicians matched the AI's decisions had the lowest mortality rates. Our model provides personalized, clinically interpretable treatment decisions for sepsis that can improve patient care.

Related papers

• Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment [5.0005174003014865]
  We propose the medical decision transformer (MeDT) to solve tasks in safety-critical settings. MeDT uses the decision transformer architecture to learn a policy for drug dosage recommendation. MeDT captures complex dependencies among a patient's medical history, treatment decisions, outcomes, and short-term effects on stability.
  arXiv  Detail & Related papers  (2024-07-28T03:40:00Z)
• Safe and Interpretable Estimation of Optimal Treatment Regimes [54.257304443780434]
  We operationalize a safe and interpretable framework to identify optimal treatment regimes. Our findings support personalized treatment strategies based on a patient's medical history and pharmacological features.
  arXiv  Detail & Related papers  (2023-10-23T19:59:10Z)
• A Conservative Q-Learning approach for handling distribution shift in sepsis treatment strategies [0.0]
  There is no consensus on what interventions work best and different patients respond very differently to the same treatment. Deep Reinforcement Learning methods can be used to come up with optimal policies for treatment strategies mirroring physician actions. The policy learned could help clinicians in Intensive Care Units to make better decisions while treating septic patients and improve survival rate.
  arXiv  Detail & Related papers  (2022-03-25T19:50:18Z)
• Optimal discharge of patients from intensive care via a data-driven policy learning framework [58.720142291102135]
  It is important that the patient discharge task addresses the nuanced trade-off between decreasing a patient's length of stay and the risk of readmission or even death following the discharge decision. This work introduces an end-to-end general framework for capturing this trade-off to recommend optimal discharge timing decisions. A data-driven approach is used to derive a parsimonious, discrete state space representation that captures a patient's physiological condition.
  arXiv  Detail & Related papers  (2021-12-17T04:39:33Z)
• Offline reinforcement learning with uncertainty for treatment strategies in sepsis [0.0]
  We present a novel application of reinforcement learning in which we identify optimal recommendations for sepsis treatment from data. Rather than a single recommendation, our method can present several treatment options. We examine learned policies and discover that reinforcement learning is biased against aggressive intervention due to the confounding relationship between mortality and level of treatment received.
  arXiv  Detail & Related papers  (2021-07-09T15:29:05Z)
• The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation [81.72197368690031]
  We present a new benchmarking suite designed specifically for medical sequential decision making. The Medkit-Learn(ing) Environment is a publicly available Python package providing simple and easy access to high-fidelity synthetic medical data.
  arXiv  Detail & Related papers  (2021-06-08T10:38:09Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Optimizing Medical Treatment for Sepsis in Intensive Care: from Reinforcement Learning to Pre-Trial Evaluation [2.908482270923597]
  Our aim is to establish a framework where reinforcement learning (RL) of optimizing interventions retrospectively allows us a regulatory compliant pathway to prospective clinical testing of the learned policies. We focus on infections in intensive care units which are one of the major causes of death and difficult to treat because of the complex and opaque patient dynamics.
  arXiv  Detail & Related papers  (2020-03-13T20:31:47Z)
• Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations [114.16762407465427]
  We introduce the Counterfactual Recurrent Network (CRN) to estimate treatment effects over time. CRN uses domain adversarial training to build balancing representations of the patient history. We show how our model achieves lower error in estimating counterfactuals and in choosing the correct treatment and timing of treatment.
  arXiv  Detail & Related papers  (2020-02-10T20:47:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.