ArticleNPJ digital medicine2024
Reinforcement learning model for optimizing dexmedetomidine dosing to prevent delirium in critically ill patients.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Reinforcement Learning for Intraoperative Hypotension Management with Consideration to Postoperative Acute Kidney Injury.Kidney360 · 2026Article
- Large language model-augmented offline reinforcement learning framework for sepsis management in critical care.NPJ digital medicine · 2026Article
- Dexmedetomidine in Palestinian intensive care units: The first multicenter cross-sector survey of sedation practices, protocol gaps, and withdrawal challenges.SAGE open medicine · 2026Article
- Optimization of glucocorticoid administration in patients with sepsis using reinforcement learning: a multicenter retrospective study.Frontiers in cellular and infection microbiology · 2026Article
- Mitochondrial dysfunction in immune cells during the perioperative period: mechanisms, emerging therapeutic strategies, and implications for multi-organ protection.Frontiers in immunology · 2026Review
- Review
- Artificial intelligence in drug development for delirium and Alzheimer's disease.Acta pharmaceutica Sinica. B · 2025Review
- Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learning.ArXiv · 2025Article
- Artificial intelligence revolutionizing anesthesia management: advances and prospects in intelligent anesthesia technology.Frontiers in medicine · 2025Review
- A Reinforcement Learning (RL)-Motivated Simulation Framework for Evaluating Vancomycin Dosing Strategies.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Delirium can result in undesirable outcomes including increased length of stays and mortality in patients admitted to the intensive care unit (ICU). Dexmedetomidine has emerged for delirium prevention in these patients; however, optimal dosing is challenging. A reinforcement learning-based Artificial Intelligence model for Delirium prevention (AID) is proposed to optimize dexmedetomidine dosing. The model was developed and internally validated using 2416 patients (2531 ICU admissions) and externally validated on 270 patients (274 ICU admissions). The estimated performance return of the AID policy was higher than that of the clinicians' policy in both derivation (0.390 95% confidence interval [CI] 0.361 to 0.420 vs. -0.051 95% CI -0.077 to -0.025) and external validation (0.186 95% CI 0.139 to 0.236 vs. -0.436 95% CI -0.474 to -0.402) cohorts. Our finding indicates that AID might support clinicians' decision-making regarding dexmedetomidine dosing to prevent delirium in ICU patients, but further off-policy evaluation is required.
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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.