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- Title
Machine Learning for Predicting Intubations in Heart Failure Patients: the Challenge of the Right Approach.
- Authors
Ghanta, Sai Nikhila; Gautam, Nitesh; Mehta, Jawahar L.; Al'Aref, Subhi J.
- Abstract
Heart failure (HF) is a significant cause of morbidity and mortality, affecting millions of people and incurring substantial healthcare costs. Acute decompensated HF can lead to respiratory failure and the need for mechanical ventilation, which is associated with increased morbidity and mortality. A recent study developed a machine learning model to predict which HF patients are at high risk for prolonged mechanical ventilation (PMV). The model showed promising results, accurately predicting the need for PMV and in-hospital mortality. However, the study has limitations, and further research is needed to validate and improve the model.
- Subjects
NONINVASIVE ventilation; ARTIFICIAL respiration; HEART failure patients; MACHINE learning; PATIENTS' rights; INTUBATION; CLINICAL decision support systems; CARDIAC intensive care
- Publication
Cardiovascular Drugs & Therapy, 2024, Vol 38, Issue 2, p211
- ISSN
0920-3206
- Publication type
Article
- DOI
10.1007/s10557-022-07423-y