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

OBJECTIVE Hypoglycemia is a hazardous diabetes-related emergency. We aimed to develop a machine learning (ML) approach for noninvasive hypoglycemia detection using voice data. 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. 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]). 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.