An exploratory machine learning study on paediatric abdominal pain phenotyping and prediction
This study identified distinct AP phenotypes and key risk factors using ML. Furthermore, the predictive ML model enabled risk stratification for paediatric AP. These analyses provide valuable insights to guide future investigations into the mechanisms of AP and may facilitate research aimed at identifying targeted interventions to improve patient outcomes.
Authors
- Huma Shehwana (ORCID: https://orcid.org/0000-0002-3665-8846)
- Animesh Acharjee (ORCID: https://orcid.org/0000-0003-2735-7010)
- Kazuya Takahashi (ORCID: https://orcid.org/0000-0002-3097-9841)
- Humayoon Shafique Satti (ORCID: https://orcid.org/0000-0003-2139-6633)
- Shuji Terai (ORCID: https://orcid.org/0000-0002-5439-635X)
- Qasim Aziz (ORCID: https://orcid.org/0000-0002-2718-2065)
- James K. Ruffle (ORCID: https://orcid.org/0000-0001-6248-7203)
- John A. Williams (ORCID: https://orcid.org/0000-0002-0357-5454)
- Georgios V. Gkoutos (ORCID: https://orcid.org/0000-0002-2061-091X)
- Michalina Lubiatowska
Institutions
- Queen Mary University of London (GB)
- Health Data Research UK (GB)
- National Hospital for Neurology and Neurosurgery (GB)
- National University of Medical Sciences (PK)
- University College London (GB)
- Niigata University (JP)
- University of Birmingham (GB)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-11-05
- DOI
- https://doi.org/10.1371/journal.pone.0336215
- Primary Topic
- Pediatric Pain Management Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00