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arXiv Machine Learning By Jiaming Liu, Cheng Ding, Jian Wu, Hongxia Xu, Daoqiang Zhang

In-Hospital Stroke Risk-State Classification from PPG-Derived Hemodynamic Features

Read the original on arXiv Machine Learning →

The study develops a 1‑D ResNet classifier that uses a fixed 17‑channel hemodynamic representation derived from photoplethysmography (PPG) to predict in‑hospital stroke risk states up to six hours before clinical recognition. Using data from MIMIC‑III and MC‑MED, the model achieved F1‑scores ranging from 0.7956 to 0.9888 across 4‑, 5‑, and 6‑hour horizons, outperforming four non‑waveform clinical and structured‑EHR comparators in all cohort‑horizon settings. Retrospective analysis showed that the PPG model’s false‑positive rates on high‑risk non‑stroke controls could be reduced through persistence aggregation, though the study does not establish a calibrated bedside alarm or a clinically validated prediction lead time.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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