Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

Autor:	Catling FJR; Institute of Healthcare Engineering, University College London, London, United Kingdom.; Division of Anaesthetics, Pain Medicine, and Intensive Care, Imperial College London, London, United Kingdom., Nagendran M; Division of Anaesthetics, Pain Medicine, and Intensive Care, Imperial College London, London, United Kingdom.; UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, United Kingdom., Festor P; UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, United Kingdom.; Department of Computing, Imperial College London, London, United Kingdom., Bien Z; School of Life Course & Population Sciences, King's College London, United Kingdom., Harris S; Department of Critical Care, University College London Hospital, London, United Kingdom.; Institute of Health Informatics, University College London, London, United Kingdom., Faisal AA; UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, United Kingdom.; Department of Computing, Imperial College London, London, United Kingdom.; Institute of Artificial and Human Intelligence, Universität Bayreuth, Bayreuth, Germany.; Department of Bioengineering, Imperial College London, London, United Kingdom., Gordon AC; Division of Anaesthetics, Pain Medicine, and Intensive Care, Imperial College London, London, United Kingdom., Komorowski M; Division of Anaesthetics, Pain Medicine, and Intensive Care, Imperial College London, London, United Kingdom.
Jazyk:	angličtina
Zdroj:	Critical care explorations [Crit Care Explor] 2024 May 06; Vol. 6 (5), pp. e1087. Date of Electronic Publication: 2024 May 06 (Print Publication: 2024).
DOI:	10.1097/CCE.0000000000001087
Abstrakt:	Large randomized trials in sepsis have generally failed to find effective novel treatments. This is increasingly attributed to patient heterogeneity, including heterogeneous cardiovascular changes in septic shock. We discuss the potential for machine learning systems to personalize cardiovascular resuscitation in sepsis. While the literature is replete with proofs of concept, the technological readiness of current systems is low, with a paucity of clinical trials and proven patient benefit. Systems may be vulnerable to confounding and poor generalization to new patient populations or contemporary patterns of care. Typical electronic health records do not capture rich enough data, at sufficient temporal resolution, to produce systems that make actionable treatment suggestions. To resolve these issues, we recommend a simultaneous focus on technical challenges and removing barriers to translation. This will involve improving data quality, adopting causally grounded models, prioritizing safety assessment and integration into healthcare workflows, conducting randomized clinical trials and aligning with regulatory requirements. (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)
Databáze:	MEDLINE
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