Exploring Novel Data-Driven Clustering Methods for Uncovering Patterns in Longitudinal Neonatal Postoperative Temperature Measurements
Exploring concordance between different machine learning techniques shows that temperature in neonates after CPB follows unique trajectories. Those exhibiting persistent hypothermia trends are at higher risk of adverse outcomes.
Authors
- Amy Jo Lisanti (ORCID: https://orcid.org/0000-0002-1826-6152)
- Susan M. Sereika (ORCID: https://orcid.org/0000-0002-7840-1352)
- Salah S. Al‐Zaiti (ORCID: https://orcid.org/0000-0002-6862-0658)
- James William Gaynor (ORCID: https://orcid.org/0000-0001-7955-5604)
- Ahmad P. Tafti (ORCID: https://orcid.org/0000-0001-9650-2862)
- Nathan T. Riek (ORCID: https://orcid.org/0000-0003-3286-560X)
- Stephanie Helman (ORCID: https://orcid.org/0000-0002-7264-2825)
- Robert Olsen
Institutions
- Children's Hospital of Philadelphia (US)
- University of Pittsburgh (US)
- Health Information Management (BE)
- University of Rochester (US)
Publication Details
- Journal
- Mayo Clinic Proceedings Digital Health
- Published
- 2025-09-25
- DOI
- https://doi.org/10.1016/j.mcpdig.2025.100270
- Primary Topic
- Thermal Regulation in Medicine
- Type
- article
- Field-Weighted Citation Impact
- 0.00