Clinical evaluation of a machine learning–based early warning system for patient deterioration
Implementing a machine learning-based early warning system in the GIM unit was associated with lower risk of non-palliative death than in the pre-intervention period. Machine learning-based early warning systems are promising technologies for improving clinical outcomes.
Authors
- Sebnem S. Kuzulugil
- Amol A. Verma (ORCID: https://orcid.org/0000-0001-6857-931X)
- Jan O. Friedrich (ORCID: https://orcid.org/0000-0001-7248-2350)
- Michael Colacci (ORCID: https://orcid.org/0000-0003-4898-6684)
- Zhen Yang (ORCID: https://orcid.org/0000-0002-0785-9382)
- Muhammad Mamdani (ORCID: https://orcid.org/0000-0001-5199-6344)
- Thérèse A. Stukel (ORCID: https://orcid.org/0000-0002-2951-1360)
- Yuna Lee (ORCID: https://orcid.org/0000-0002-4339-9301)
- Joshua Murray (ORCID: https://orcid.org/0000-0002-9233-4934)
- Jonathan Ailon (ORCID: https://orcid.org/0000-0002-2751-7865)
- Chloé Pou-Prom (ORCID: https://orcid.org/0000-0002-5719-7786)
- Shirley Bell
Institutions
- Chartered Institute of Management Accountants (GB)
Publication Details
- Journal
- Canadian Medical Association Journal
- Published
- 2024-09-15
- DOI
- https://doi.org/10.1503/cmaj.240132
- Citations
- 22
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 8.51