Dharma: A novel, clinically grounded machine learning framework for pediatric appendicitis—Diagnosis, severity assessment and evidence-based clinical decision support
- DOI
- 10.1371/journal.pdig.0000908
- Published
- 2026-01-21
- Container
- PLOS Digital Health
- Publisher
- Public Library of Science (PLoS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1371/journal.pdig.0000908,
title = {Dharma: A novel, clinically grounded machine learning framework for pediatric appendicitis—Diagnosis, severity assessment and evidence-based clinical decision support},
author = {Anup Thapa Kshetri and Subash Pahari and Shashank Timilsina and Binay Chapagain},
year = {2026},
journal = {PLOS Digital Health},
doi = {10.1371/journal.pdig.0000908},
url = {https://doi.org/10.1371/journal.pdig.0000908}
}RIS
TY - JOUR TI - Dharma: A novel, clinically grounded machine learning framework for pediatric appendicitis—Diagnosis, severity assessment and evidence-based clinical decision support AU - Anup Thapa Kshetri AU - Subash Pahari AU - Shashank Timilsina AU - Binay Chapagain PY - 2026 JO - PLOS Digital Health DO - 10.1371/journal.pdig.0000908 UR - https://doi.org/10.1371/journal.pdig.0000908 ER -
APA
Kshetri, A. T., Pahari, S., Timilsina, S., & Chapagain, B. (2026). Dharma: A novel, clinically grounded machine learning framework for pediatric appendicitis—Diagnosis, severity assessment and evidence-based clinical decision support. PLOS Digital Health. https://doi.org/10.1371/journal.pdig.0000908
Source records
- crossref · retrieved 2026-09-25T20:17:58.952Z