aceDemographic subgroup-aware analysis of blood pressure predictive models
Author summary Recent advancements in machine learning and artificial intelligence have led to the development of numerous predictive models to derive blood pressure estimates from PPG signals. Generally, the performance of blood pressure (BP) prediction models is reported as an average across the entire dataset without accounting for the distribution of different populations within the data. However, recent studies have highlighted significant disparities in the performance of machine learning models across div…
Blood pressure prediction models built on PPG signals are usually scored by dataset-wide averages, which can mask performance gaps between demographic groups and raise fairness concerns.
The supplied excerpt cuts off in the abstract and contains no results, so the extent of subgroup disparities in blood pressure models is not shown here.
Evidence
- JournalismPLOS (Public Library of Science)2026-10-15
How should this claim be treated?
Truvace Impact Record TRV-2026-1497, v1: “Demographic subgroup-aware analysis of blood pressure predictive models.” Truvace, 2026-10-15. /record/TRV-2026-1497 (accessed at citation time). sha256 e3856921f0dcb8bd…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-1497 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.