Human-Computer Interaction Ph.D. Thesis Proposal
Speaker
PRERNA CHIKERSAL
Ph.D. Student
Human-Computer Interaction Institute
Carnegie Mellon University
When
-
Where
In Person and Virtual Presentation
Description
Mental health disorders are increasing in occurrence. They are the largest cause of disability worldwide and the strongest predictor of suicide. Despite their prevalence, the majority of affected people either never seek support, or receive limited to no support from under resourced health systems. Further, finding the right treatment for a specific person is a time consuming and inefficient process, as most interventions are based on studies that find the best treatment for a “typical” patient, rather than tailoring interventions to the patient’s genes, environment and lifestyle. Hence, to increase access and efficiency of mental health care, there is a need to develop digital tools that make medicine more precise by using data-driven insights and predictions to aid diagnosis, monitoring, prevention, and treatment of mental health disorders.
This PhD thesis focuses on developing computational methods and models that use user-generated data from multiple data sources including passively sensed smartphone and wearable sensor data, text messages exchanged between users, and the users' interaction logs with web or mobile apps, to analyze or predict mental health outcomes with the goal of making the diagnosis and treatment of mental health disorders more efficient and precise. The biggest problem in precision mental health care is the curse of dimensionality in terms of the feature space, outcomes, and patients. That is, to tailor diagnosis, prevention and treatment to each individual, we need to collect and analyze enormous amounts of data associated with the person's behaviors, environment, and other in-situ features, the person's outcomes (e.g. multiple morbidities, outcomes related to mental and physical health), and contextual, occupational, demographic, and other confounding variables that can affect the patient's health. The curse of dimensionality when working with such multimodal and multidimensional data lowers the reliability of analysis and modeling, and decreases the interpretability of the findings. In this thesis, I address the curse of dimensionality challenge through 6 studies (4 completed and 2 proposed).
Upon completion of the 6 studies, my thesis will have made the following contributions: (1) Presented a feature selection method that mitigates the curse of dimensionality in the feature space by decomposing and iteratively reducing the feature space during feature selection. (2) Demonstrated the generalizability of the approach in detecting depression, change in depression, and loneliness, as well as forecasting these outcomes several weeks in advance. (3) Demonstrated that behavioral changes resulting from the stay-at-home mandates during the pandemic are predictive of health outcomes during the stay-at-home period for patients with multiple sclerosis. (4) Demonstrated how we can categorize supporters or identify patient phenotypes based on multiple comorbid outcomes, thereby mitigating the curse of dimensionality with respect to multimorbidities. (5) Presented a method that visualizes and identifies support strategies that work best in an online mental health intervention for patients in a specific context or situation. (6) Presented a method that improves model performance and/or interpretability for predicting health outcomes by leveraging the similarities and differences in patient characteristics.
My work has significant implications for the detection of health outcomes using longitudinal behavioral data and limited ground truth. My work also has implications for early and more personalized interventions for mental health disorders.
Thesis Committee:
Anind Dey (Co-chair, University of Washington)
Mayank Goel (Co-chair, HCII)
Geoff Kauffman (HCII)
Andrew Campbell (Dartmouth College)
Mary Czerwinski (Microsoft Research)
In Person and Zoom Participation. See announcement.