Predicting mechanical ventilation duration in ICU patients: A data-driven machine learning approach for clinical decision-making
Background Mechanical ventilation is essential in intensive care units (ICUs) but poses risks such as ventilator-associated complications and high costs. The accuracy of predicting mechanical ventilation duration using clinical information is limited. Predicting ventilation duration accurately can a...
Saved in:
| Main Authors: | Shivi Mendiratta, Vinay Gandhi Mukkelli, Esha Baidya Kayal, Puneet Khanna, Amit Mehndiratta |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-06-01
|
| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076251352988 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Explainable machine learning models for mortality prediction in patients with sepsis in tertiary care hospital ICU in low- to middle-income countries
by: Saumya Diwan, et al.
Published: (2025-06-01) -
Non-Invasive Ventilation Failure in Pediatric ICU: A Machine Learning Driven Prediction
by: Maria Vittoria Chiaruttini, et al.
Published: (2024-12-01) -
Comparative efficacy of remifentanil and fentanyl in mechanically ventilated ICU patients: a systematic review and meta-analysis on ventilation duration and delirium incidence
by: Hiromu Okano, et al.
Published: (2025-06-01) -
ICU ‘Magic Numbers’: The Role of Biomarkers in Supporting Clinical Decision-Making
by: Francesco Cipulli, et al.
Published: (2025-04-01) -
Comparisons of Metabolic Load between Adaptive Support Ventilation and Pressure Support Ventilation in Mechanically Ventilated ICU Patients
by: Yen-Huey Chen, et al.
Published: (2020-01-01)