Prediction of Respiratory Decompensation in Patients Receiving Home Mechanical Ventilation: Machine Learning Model Development and Validation Study
This study demonstrates the feasibility of predicting respiratory decompensation using data from HMV telemonitoring systems. Tree-based ensemble models, particularly random forests, provided the most balanced performance, while SHAP analysis offered clinically relevant insights. Although performance was moderate, the findings support further development of predictive tools to enable timely telemedical interventions. Limitations include sample size, missing physiological parameters, and a single-center design. Future research should expand to multicenter datasets and incorporate additional clinical variables to enhance model robustness and generalizability.
Authors
- Francesc López Seguí (ORCID: https://orcid.org/0000-0003-0977-0215)
- Ignasi García-Olivé (ORCID: https://orcid.org/0000-0001-7698-5107)
- Aída Selfa Muñoz (ORCID: https://orcid.org/0000-0001-6722-0639)
- Marina Galdeano Lozano (ORCID: https://orcid.org/0000-0001-8604-0174)
- Antoni Rosell (ORCID: https://orcid.org/0000-0003-0877-7191)
- Nerea Berbel Casado (ORCID: https://orcid.org/0009-0001-5021-3077)
- Natalia Muñoz Moruno (ORCID: https://orcid.org/0009-0003-4636-220X)
Institutions
- Universitat de Vic - Universitat Central de Catalunya (ES)
- Universitat Autònoma de Barcelona (ES)
- Universitat Pompeu Fabra (ES)
- Centre de Recerca en Economia Internacional (ES)
- Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol (ES)
- Hospital Universitari Germans Trias i Pujol (ES)
Publication Details
- Journal
- JMIR Formative Research
- Published
- 2025-11-21
- DOI
- https://doi.org/10.2196/78941
- Primary Topic
- Respiratory Support and Mechanisms
- Type
- article
- Field-Weighted Citation Impact
- 0.00