Clinical pathway data engineering framework (CPDEF): a reproducible methodology for constructing machine learning-ready case-mix datasets from hospital information systems
- DOI
- 10.1016/j.mex.2026.104163
- Published
- 2026-12
- Container
- MethodsX
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.mex.2026.104163,
title = {Clinical pathway data engineering framework (CPDEF): a reproducible methodology for constructing machine learning-ready case-mix datasets from hospital information systems},
author = {Suryanto Nugroho and Raden Venantius Hari Ginardi and I Ketut Eddy Purnama},
year = {2026},
journal = {MethodsX},
doi = {10.1016/j.mex.2026.104163},
url = {https://doi.org/10.1016/j.mex.2026.104163}
}RIS
TY - JOUR TI - Clinical pathway data engineering framework (CPDEF): a reproducible methodology for constructing machine learning-ready case-mix datasets from hospital information systems AU - Suryanto Nugroho AU - Raden Venantius Hari Ginardi AU - I Ketut Eddy Purnama PY - 2026 JO - MethodsX DO - 10.1016/j.mex.2026.104163 UR - https://doi.org/10.1016/j.mex.2026.104163 ER -
APA
Nugroho, S., Ginardi, R. V. H., & Purnama, I. K. E. (2026). Clinical pathway data engineering framework (CPDEF): a reproducible methodology for constructing machine learning-ready case-mix datasets from hospital information systems. MethodsX. https://doi.org/10.1016/j.mex.2026.104163
Source records
- crossref · retrieved 2026-09-25T20:42:13.917Z