ArticleNPJ digital medicine2025
Challenges with reinforcement learning model transportability for sepsis treatment in emergency care.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Large language model-augmented offline reinforcement learning framework for sepsis management in critical care.NPJ digital medicine · 2026Article
- Risk-Stratified Tiered Intervention Among Chinese Clinical High-Risk for Psychosis Patients and Attenuated Negative Symptoms as a Predictor of 1-Year Psychosis Conversion: A Retrospective Propensity-Matched Real-World Cohort Study.Neuropsychiatric disease and treatment · 2026Article
- Healthcare 5.0-Driven Clinical Intelligence: The Learn-Predict-Monitor-Detect-Correct Framework for Systematic Artificial Intelligence Integration in Critical Care.Healthcare (Basel, Switzerland) · 2025Review
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Authors and funding
7 authors.
Funding
Abstract
Pivotal moments in sepsis care occur in the emergency department (ED), however, and it is unclear whether ED data is adequate to inform reinforcement learning (RL) models. We evaluated the early opportunity for the AI Clinician, a validated ICU-based RL-model, as a use case. Amongst emergency sepsis patients, model parameters were often missing and invariably measured. Current iterations of RL-models trained on ICU data face challenges in emergency sepsis care.
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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.