Dharma: A novel, clinically grounded machine learning framework for pediatric appendicitis—Diagnosis, severity assessment and evidence-based clinical decision support

Anup Thapa Kshetri, Subash Pahari, Shashank Timilsina, Binay Chapagain

Open source

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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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

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