Informing future intensive care trials with machine learning
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AI might help researchers design and enrol tailored patient subgroups for precise RCTs.
“Instead of using large, heterogeneous trial populations, AI might help researchers design and enrol tailored patient subgroups for precise RCTs [63, 64].”
From Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22. (2025) · cites this paper as [63]
Cites
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1 of the 16 references couldn’t be matched to a known paper.
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Sepsis biomarkers and diagnostic tools with a focus on machine learning
Komorowski, M., Green, A., Tatham, K. C., Seymour, C. W., & Antcliffe, D. B. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104394 · Cited by 160 · see citing works
Self-citationImplementing Artificial Intelligence in Critical Care Medicine: a consensus of 22.
Cecconi, M., Greco, M., Shickel, B., Angus, D. C., Bailey, H., Bignami, E., Calandra, T., Celi, L. A., Einav, S., Elbers, P., et al. (2025) · Critical Care · doi:10.1186/s13054-025-05532-2 · Cited by 80 · see citing works
“Instead of using large, heterogeneous trial populations, AI might help researchers design and enrol tailored patient subgroups for precise RCTs [63, 64].”
Self-citationMachine learning in anaesthesia: reactive, proactive… predictive!
Gambús, P. L., & Jaramillo, S. (2019) · British Journal of Anaesthesia · doi:10.1016/j.bja.2019.07.009 · Cited by 32 · see citing works
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Artificial Intelligence Applications in Space Medicine
Cheung, H. C., Louche, C. D. D., & Komorowski, M. (2023) · Aerospace Medicine and Human Performance · doi:10.3357/amhp.6178.2023 · Cited by 24 · see citing works
Self-citationAcute on chronic liver failure: prognostic models and artificial intelligence applications
Gary, P. J., Lal, A., Simonetto, D. A., Gajic, O., & de Moraes, A. G. (2023) · Hepatology Communications · doi:10.1097/hc9.0000000000000095 · Cited by 6 · see citing works
Longitudinal blood transcriptome profiling reveals immune dynamics in sepsis
Hwang, J., Cha, J. H., Ahn, S., Park, J.-H., Kim, S., Jung, M., Choi, Y., Seok, H., Choi, W. S., Nam, M.-H., et al. (2026) · BMC Infectious Diseases · doi:10.1186/s12879-026-13416-1 · Cited by 1 · see citing works
Implementing responsible artificial intelligence-driven precision medicine in critical care
Ennouri, E., Boussarsar, M., Saida, I. B., Meddeb, K., & Toumi, R. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-36554-6.00021-0
- Authors
- Komorowski, M., & Lemyze, M.
- Affiliations
- Charing Cross Hospital (GB)
- Centre Hospitalier d'Arras (FR)
- Corresponding author
- Komorowski, Matthieu
- Venue
- British Journal of Anaesthesia
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 123
- Issue
- 1
- Pages
- 14-16
- ISSN
- 0007-0912 (ISSN-L)
- OpenAlex
- W2943902158
- Cited by
- 8
- References
- 16
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 1 · 2023: 2 · 2022: 1 · 2019: 1
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- British Journal of Anaesthesia (publishedVersion)
- PubMed (publishedVersion)
- Spiral (Imperial College London) (submittedVersion, cc-by-nc-nd)
- Funders
- Engineering and Physical Sciences Research Council (1675244)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Respiratory Support and Mechanisms
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- machine learning, randomized controlled trials, acute respiratory distress syndrome, sepsis, ICU-acquired weakness, delirium, open lung ventilation, positive end-expiratory pressure, mechanical ventilation, trial design, negative trial results
- MeSH terms
- Machine Learning, Critical Care, Humans, Respiratory Distress Syndrome
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