Machine Learning Methods to Identify Pediatric Shunt Malfunction in the Acute Care Setting and the Development of ShuntGPT
Advanced ML models generally outperformed traditional statistical analyses, albeit with concerns about overfitting due to the extensive variable set. SGPT, in particular, showed superior performance, likely due to its capacity to interpret nuanced text. This model represents a promising tool to enhance decision-making in pediatric acute care settings regarding shunt malfunctions.
Authors
- Kelly Jiang (ORCID: https://orcid.org/0000-0002-1144-7882)
- Ryan P. Lee (ORCID: https://orcid.org/0000-0002-2517-9559)
- Jacob S. Shaw (ORCID: https://orcid.org/0000-0002-7906-9945)
- Anita Kalluri
- Rama J. Alawneh
- Kurt Lehner (ORCID: https://orcid.org/0000-0001-7916-1357)
- Joan Yea
- Sai Chandan Reddy
- Foad Kazemi
- Eric M. Jackson
Institutions
- Johns Hopkins University (US)
- Mutah University (JO)
- Johns Hopkins Medicine (US)
Publication Details
- Journal
- Neurosurgery
- Published
- 2026-02-16
- DOI
- https://doi.org/10.1227/neu.0000000000003962
- Primary Topic
- Cerebrospinal fluid and hydrocephalus
- Type
- article
- Field-Weighted Citation Impact
- 0.00