ArticleJournal of clinical monitoring and computing2025
Safe automation of interfering medical treatments via control barrier functions and reachability analysis: a fluid resuscitation-sedation-vasopressor infusion case study.
Article in Journal of clinical monitoring and computing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Development of a vasopressor control module for testing hemorrhagic shock resuscitation controllers.Biomedical engineering online · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Despite advances made toward the automation of medical treatments, existing work has predominantly focused on the automation of individual medical treatments while overlooking interferences between them. In our prior work, we developed an automation architecture to administer interfering medical treatments with safety, which (i) monitors internal physiological state of a patient using an extended Kalman filter, (ii) mediates medical treatments to bound the estimated internal state within a prescribed safe regime based on control barrier functions, and (iii) treats the patient to a prespecified treatment target through a number of intermediate targets derived from reachability analysis. The goal of this work was to investigate the scalability of this automation architecture in more complex and challenging medical treatment scenarios with a number of conflicts. Using a critical care resuscitation scenario including fluid resuscitation and intravenous sedative-vasopressor infusion, we examined if our automation architecture can achieve treatment goals while ascertaining the safety of internal state in a large number of diverse in silico patients. The results suggested that (i) the extended Kalman filter could continuously monitor a patient's internal physiological state, (ii) the control barrier functions could mediate interfering medical treatments and protect patients against unsafe internal physiological state, and (iii) the reachability analysis could treat a patient as closely as possible to a treatment target while ensuring the safety of the patient's internal physiological state, all despite complex and entangled conflicts between them. Our automation architecture may provide a viable means to autonomously de-conflict interfering medical treatments.
Indexed as
Identifiers
40050565What 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.