Introduction to using symbolic regression for interpretable machine learning in healthcare

Michael Ferguson, Feifan Liu, Honghuang Lin, David D. McManus, Ben S. Gerber

Open source

DOI: 10.1038/s43856-026-01914-x
Published: 2026-10-09
Container: Communications Medicine
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1038/s43856-026-01914-x,
  title = {Introduction to using symbolic regression for interpretable machine learning in healthcare},
  author = {Michael Ferguson and Feifan Liu and Honghuang Lin and David D. McManus and Ben S. Gerber},
  year = {2026},
  journal = {Communications Medicine},
  doi = {10.1038/s43856-026-01914-x},
  url = {https://doi.org/10.1038/s43856-026-01914-x}
}

RIS

TY  - JOUR
TI  - Introduction to using symbolic regression for interpretable machine learning in healthcare
AU  - Michael Ferguson
AU  - Feifan Liu
AU  - Honghuang Lin
AU  - David D. McManus
AU  - Ben S. Gerber
PY  - 2026
JO  - Communications Medicine
DO  - 10.1038/s43856-026-01914-x
UR  - https://doi.org/10.1038/s43856-026-01914-x
ER  -

APA

Ferguson, M., Liu, F., Lin, H., McManus, D. D., & Gerber, B. S. (2026). Introduction to using symbolic regression for interpretable machine learning in healthcare. Communications Medicine. https://doi.org/10.1038/s43856-026-01914-x

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crossref · retrieved 2026-11-10T14:13:58.062Z