Listening to Hypoglycemia: Voice as a Biomarker for Detection of a Medical Emergency Using Machine Learning

MCML Authors

Stefan Feuerriegel

Prof. Dr.

Principal Investigator

Artificial Intelligence in Management

Abstract

OBJECTIVE: Hypoglycemia is a hazardous diabetes-related emergency. We aimed to develop a machine learning (ML) approach for noninvasive hypoglycemia detection using voice data.<br>RESEARCH DESIGN AND METHODS: We collected voice data (540 recordings) with a smartphone in standardized euglycemia and hypoglycemia in two sequential clinical studies in people with type 1 diabetes. Using these data, we trained and evaluated an ML approach to detect hypoglycemia solely based on voice features.<br>RESULTS: Twenty-two individuals were included (11 female, age 37.3 ± 12.4 years, HbA1c 7.1 ± 0.5%). The ML approach detected hypoglycemia noninvasively with high accuracy (area under the receiver operating characteristic curve 0.90 ± 0.12 for reading a text aloud and 0.87 ± 0.15 for rapid repetition of syllables [diadochokinetic task]).<br>CONCLUSIONS: An ML approach exclusively based on voice data allows for noninvasive hypoglycemia detection, corroborating the potential of ML-based approaches to infer acute health states through voice.

article LHM+25c

---

Diabetes Care

dc251680. Nov. 2025.

---

Authors

V. Lehmann • M. Hilpert • Z. Mostaani • S. Hovsepyan • E. Wallace • C. Verzat • S. Feuerriegel • M. Kraus • J. Rosenthal • G. Yilmaz • M. M. Doss • C. Stettler

---

Links

DOI

---

Research Area

 A1 | Statistical Foundations & Explainability