Predicting Escalation of Care for Childhood Pneumonia Using Machine Learning: Retrospective Analysis and Model Development
This study demonstrates the feasibility of applying ML techniques to create a prognostic care decision tool for childhood pneumonia. It provides early identification of cases requiring escalation of care by combining foundational clinical skills with data science methods.
Authors
- Busra Köseoğlu (ORCID: https://orcid.org/0000-0003-3115-0020)
- Oğuzhan Serin (ORCID: https://orcid.org/0000-0002-3276-748X)
- Ahmet Fatih Deveci (ORCID: https://orcid.org/0000-0002-3044-8397)
- Izzet Turkalp Akbasli (ORCID: https://orcid.org/0000-0003-3055-7355)
- Yasemin Özsürekçi (ORCID: https://orcid.org/0000-0001-8662-6909)
- Selahattin Çetin (ORCID: https://orcid.org/0000-0002-5627-9296)
- Muhsin Zahid Ugur (ORCID: https://orcid.org/0000-0003-2676-8539)
Institutions
- Sağlık Bilimleri Üniversitesi (TR)
- Hacettepe University (TR)
Publication Details
- Journal
- JMIRx Med
- Published
- 2025-03-04
- DOI
- https://doi.org/10.2196/57719
- Citations
- 4
- Primary Topic
- Pneumonia and Respiratory Infections
- Type
- article
- Field-Weighted Citation Impact
- 5.42