ArticleFrontiers in physiology2024
AI algorithm for personalized resource allocation and treatment of hemorrhage casualties.
Article in Frontiers in physiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Systematic review and meta-analysis of machine learning models predicting massive hemorrhage protocol in trauma.Scientific reports · 2026Pooled it
- A model to simulate cardio-respiratory responses to fentanyl analgesia after traumatic injury.Frontiers in physiology · 2026Article
- A reinforcement learning algorithm to optimize resource utilization in combat casualty care.Scientific reports · 2025Article
- Opportunities for Artificial Intelligence in Operational Medicine: Lessons from the United States Military.Bioengineering (Basel, Switzerland) · 2025Review
- A model to simulate human cardio-respiratory responses to different fluid resuscitation treatments after hemorrhagic injury.Frontiers in physiology · 2025Article
- Neutrophil-to-lymphocyte ratio and systemic inflammation response index as biomarkers for the clinical outcomes of intracerebral hemorrhagic stroke patients: a prospective cohort study.Frontiers in neurology · 2025Article
- A model to simulate human cardio-respiratory responses to ketamine analgesia.Frontiers in physiology · 2025Article
- A model to simulate human cardio-respiratory responses to airway obstruction.Frontiers in physiology · 2025Article
- Navigating Hemorrhagic Shock: Biomarkers, Therapies, and Challenges in Clinical Care.Biomedicines · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A deep neural network-based artificial intelligence (AI) model was assessed for its utility in predicting vital signs of hemorrhage patients and optimizing the management of fluid resuscitation in mass casualties. With the use of a cardio-respiratory computational model to generate synthetic data of hemorrhage casualties, an application was created where a limited data stream (the initial 10 min of vital-sign monitoring) could be used to predict the outcomes of different fluid resuscitation allocations 60 min into the future. The predicted outcomes were then used to select the optimal resuscitation allocation for various simulated mass-casualty scenarios. This allowed the assessment of the potential benefits of using an allocation method based on personalized predictions of future vital signs
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What OpenQuestion holds
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.