Murphy, S.A., & Nahum-Shani, I. (2026, January) Just In Time Adaptive Interventions: Opportunities, Misconceptions, and the Promise of AI-based Algorithms. NIH Digital Health Scientific Interest Group (SIG)’s Speaker Series. NIH. Bethesda, MD. You can watch this presentation here: https://videocast.nih.gov/watch/0b1c35f9-0852-11f1-9f14-124f0a52e769
Authors: Mehrdad Dadgostar, Lindsay C. Hanford, Maryam Tavakoli, Steven E. Arnold, David H. Salat, Tatiana Sitnikova, Pia Kivisakk Webb, Jordan R. Green, Hengru Liu, Brian D. Richburg, Mariam Tkeshelashvili, Marziye Eshghi Abstract INTRODUCTION We tested whether spontaneous speech acoustics provide a scalable digital marker of biologically defined Alzheimer’s disease (AD) risk. METHODS Forty-nine cognitively unimpaired older adults were stratified within APOE genotype into Low-, Moderate-, and High-Risk groups based on log₁₀-transformed plasma p-tau217. Acoustic features were extracted from spontaneous speech and entered into multiclass SVM classifiers with leave-one-out cross-validation, with and without genetic-algorithm feature selection and age. Parallel models using neuropsychological measures were evaluated for comparison. Feature contributions were interpreted using SHAP. RESULTS Speech-based models substantially outperformed cognition-only models and exceeded chance performance for three-group classification (33.3%), achieving up to 77% accuracy compared with 47%…
Bernard Ofosuhene, Clinical Research Coordinator, Mary Technology Inc.
Sherman Huang, PhD, Professor, Worcester Polytechnic Institute.
Fiona Yuan, PhD, Assistant Professor, Worcester Polytechnic Institute.
Yadid Ayzenberg, PhD, Founder & CEO, SOND.
Pritom Chowdhury & Zhenyu Lei, Graduate Students, 2D Bioelectronics Lab, UMass Amherst.