Leveling Up: A Review of Machine Learning Models in the Cardiac ICU - 20/09/23
, Parker Wilson, BS b, Yash Suri, BS c, Hunter Culbert, BS c, Jessa Deckwa, BS d, Amina Khalpey, PhD e, Brynne Rozell, BS bAbstract |
Machine learning has emerged as a significant tool to augment the medical decision-making process. Studies have steadily accrued detailing algorithms and models designed using machine learning to predict and anticipate pathologic states. The cardiac intensive care unit is an area where anticipation is crucial in the division between life and death. In this paper, we aim to review important studies describing the utility of machine learning algorithms to describe the future of artificial intelligence in the cardiac intensive care unit, especially in regards to the prediction of successful ventilatory weaning, acute respiratory distress syndrome, arrhythmia, and acute kidney injury.
Le texte complet de cet article est disponible en PDF.Keywords : Acute kidney injury, ARDS, Artificial intelligence, Atrial fibrillation, Cardiac ICU, Machine learning, Precision health care, Sepsis, Ventilator weaning
Plan
| Funding: None. |
|
| Conflicts of Interest: None for any of the authors of this manuscript. |
|
| Authorship: All authors had access to the data, and participated in the research, writing, and preparation of the manuscript. |
Vol 136 - N° 10
P. 979-984 - octobre 2023 Retour au numéro
Article précédent
- Should Antihypertensive Medications Be Routinely Administered in the Nighttime Instead of Daytime?
- Hardik Patel, Felix Berglund, Luke Laffin, Nicholas Ruthmann
- Transforming Health Care from Volume to Value: Leveraging Care Coordination Across the Continuum
- Sharon Klein, Kevin P. Eaton, Benjamin E. Bodnar, Sara C. Keller, Paul Helgerson, Andrew S. Parsons
Bienvenue sur EM-consulte, la référence des professionnels de santé.
L’accès au texte intégral de cet article nécessite un abonnement.
Déjà abonné à cette revue ?