This talk was held on September 12, 2019 as a part of the MLFL series, hosted by the Center for Data Science, UMass Amherst. Full Title of the Talk:
Probabilistic Latent Variable Modeling for Predicting Future Well-Being and Assessing Behavioral Influences on Stress
Abstract of the Talk:
Health research has an increasing focus on promoting well-being and positive mental health, to prevent disease and to more effectively treat disorders. The availability of rich multi-modal datasets and advances in machine learning methods are now enabling data science research to begin to objectively assess well-being. However, most existing studies focus on detecting the current state or predicting the future state of well-being using stand-alone health behaviors. There is a need for methods that can handle a complex combination of health behaviors, as arise in real-world data.
Building on our previous work where we predict future well-being, in this talk, I'll present a framework to 1) map multi-modal messy data collected in the "wild" to meaningful feature representations of health behavior, 2) uncover latent patterns comprising multiple health behaviors that best predict well-being, and 3) propose how these patterns may be used to recommend healthy behaviors to participants. We show how to use supervised latent Dirichlet allocation (sLDA) to model the observed behaviors, and we apply variational inference to uncover the latent patterns. Implementing and evaluating the model on 5,397 days of data from a group of 244 college students, we find that these latent patterns are indeed predictive of self-reported stress, one of the largest components affecting well-being. We investigate the modifiable behaviors present in these patterns and uncover some ways in which the factors work together to influence well-being.
This work contributes a new method using objective data analysis to help individuals monitor their well-being using real-world measurements. Insights from this study advance scientific knowledge on how combinations of daily modifiable human behaviors relate to human well-being.
About the Speaker:
Ehi Nosakhare is an AI Data Scientist at Microsoft's New England Research and Development Center (NERD). She designs, develops and leads the implementation of machine learning solutions in application projects for Microsoft's products and services. In August 2018, she earned her Ph.D. in Electrical Engineering and Computer Science (EECS) from the Massachusetts Institute of Technology (MIT), Cambridge, MA. Her PhD research focused on probabilistic latent variable models and applying them to understand subjective well-being. She is generally interested in developing interpretable ML models and using these models to solve real world problems, as a result, she is curious about the ethical implications of AI/ML. Ehi got her S.M. in EECS from MIT, and graduated with a B.Sc. in Electrical Engineering, summa cum laude, from Howard University, Washington DC. As a student, she completed internships at Microsoft and IBM T. J. Watson Research Center. She is a recipient of a best paper award at the NeurIPS ML for Healthcare Workshop. In 2017, she was an organizer for the Women in Machine Learning (WiML) workshop, co-located with NeurIPS. Ehi has been honored as a Tau Beta Pi Scholar and Fellow. In her spare time, she enjoys reading and re-learning to play the cello.
About Machine Learning and Friends Lunch:
MLFL is a lively and interactive forum held weekly where friends of the UMass Amherst machine learning community can sit down, have lunch, and give or hear a 50-minute presentation on recent machine learning research.
This semester of the UMass MLFL series has been graciously sponsored by our friends at Oracle Labs.
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