Interpretable machine learning for identifying determinants of high hypertension burden under extreme heat vulnerability: evidence from Maryland, USA.

Peng B

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DOI: 10.3389/fpubh.2026.1894531
Published: 2026
Container: Frontiers in public health
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3389/fpubh.2026.1894531,
  title = {Interpretable machine learning for identifying determinants of high hypertension burden under extreme heat vulnerability: evidence from Maryland, USA.},
  author = {Peng B},
  year = {2026},
  journal = {Frontiers in public health},
  doi = {10.3389/fpubh.2026.1894531},
  url = {https://doi.org/10.3389/fpubh.2026.1894531}
}

RIS

TY  - JOUR
TI  - Interpretable machine learning for identifying determinants of high hypertension burden under extreme heat vulnerability: evidence from Maryland, USA.
AU  - Peng B
PY  - 2026
JO  - Frontiers in public health
DO  - 10.3389/fpubh.2026.1894531
UR  - https://doi.org/10.3389/fpubh.2026.1894531
ER  -

APA

B, P. (2026). Interpretable machine learning for identifying determinants of high hypertension burden under extreme heat vulnerability: evidence from Maryland, USA.. Frontiers in public health. https://doi.org/10.3389/fpubh.2026.1894531

Source records

pubmed · retrieved 2026-09-26T08:35:49.845Z
europe-pmc · retrieved 2026-09-26T08:35:49.853Z
doaj · retrieved 2026-09-26T08:35:49.840Z