ArticleNPJ digital medicine2023
A value-based deep reinforcement learning model with human expertise in optimal treatment of sepsis.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Value of AI in Critical Care Using Real-World Evidence on Intensive Care Unit Mortality Prediction: Cost-Utility Analysis.Journal of medical Internet research · 2026Article
- Self-powered intelligence for personalized healthcare.National science review · 2026Review
- Augmenting large language models with clinical knowledge graph for personalized perioperative fluid therapy question answering.PLOS digital health · 2026Article
- Optimizing hepatitis C diagnosis through reinforcement learning feature selection and multi-model machine learning evaluation.Scientific reports · 2026Article
- Off by a beat: the effects of temporal misalignment in reinforcement learning for sepsis treatment.NPJ digital medicine · 2026Article
- Liver transplant donor-recipient matching with offline reinforcement learning.NPJ digital medicine · 2026Article
- A reinforcement learning-guided interpretable method for postoperative sepsis prediction with Hilbert-Schmidt Independence Criterion.Frontiers in big data · 2026Article
- Towards AI-based precision rehabilitation via contextual model-based reinforcement learning.Journal of neuroengineering and rehabilitation · 2025Article
- Reinforcement Learning and Its Clinical Applications Within Healthcare: A Systematic Review of Precision Medicine and Dynamic Treatment Regimes.Healthcare (Basel, Switzerland) · 2025Review
- Optimizing Vital Signs in Patients With Traumatic Brain Injury: Reinforcement Learning Algorithm Development and Validation.Journal of medical Internet research · 2025Article
- Reinforcement learning for healthcare operations management: methodological framework, recent developments, and future research directions.Health care management science · 2025Review
- The power of artificial intelligence for managing pandemics: A primer for public health professionals.The International journal of health planning and management · 2025Review
- A Primer on Reinforcement Learning in Medicine for Clinicians.NPJ digital medicine · 2024Review
- Deep reinforcement learning extracts the optimal sepsis treatment policy from treatment records.Communications medicine · 2024Article
- Reinforcement learning model for optimizing dexmedetomidine dosing to prevent delirium in critically ill patients.NPJ digital medicine · 2024Article
- Individualized decision making in on-scene resuscitation time for out-of-hospital cardiac arrest using reinforcement learning.NPJ digital medicine · 2024Article
- Investigating computational models for diagnosis and prognosis of sepsis based on clinical parameters: Opportunities, challenges, and future research directions.Journal of intensive medicine · 2024Review
- Metabolic modelling as a powerful tool to identify critical components of Pneumocystis growth medium.PLoS computational biology · 2024Article
- A practical guide to the implementation of artificial intelligence in orthopaedic research-Part 2: A technical introduction.Journal of experimental orthopaedics · 2024Review
- Artificial intelligence and machine learning's role in sepsis-associated acute kidney injury.Kidney research and clinical practice · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep Reinforcement Learning (DRL) has been increasingly attempted in assisting clinicians for real-time treatment of sepsis. While a value function quantifies the performance of policies in such decision-making processes, most value-based DRL algorithms cannot evaluate the target value function precisely and are not as safe as clinical experts. In this study, we propose a Weighted Dueling Double Deep Q-Network with embedded human Expertise (WD3QNE). A target Q value function with adaptive dynamic weight is designed to improve the estimate accuracy and human expertise in decision-making is leveraged. In addition, the random forest algorithm is employed for feature selection to improve model interpretability. We test our algorithm against state-of-the-art value function methods in terms of expected return, survival rate, action distribution and external validation. The results demonstrate that WD3QNE obtains the highest survival rate of 97.81% in MIMIC-III dataset. Our proposed method is capable of providing reliable treatment decisions with embedded clinician expertise.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.