ABOUT THE TALK Digital biomarker technology is an emerging pragmatic approach to permit objective, ecologically valid, and long-term continuous measurement of cognitive health status, rendering it as one of the promising technologies for early MCI detection. Despite its potential, it is nontrivial to encode, extract and combine predictive information from these digital biomarker technologies.
In this dissertation, the overall machine learning task for digital biomarker technology to detect MCI is organized into three research problems to be successively tackled. First, the knowledge gaps in the understanding of the state-of-the-art (SOTA) ML techniques employed for digital biomarker technologies to detect cognitive decline were addressed by conducting a systematic review and meta-analysis of relevant studies. Next, the technical challenge of tackling the prevalent small subject sample size real-world data problem in digital biomarker studies was tackled via investigating a unique ML technique, namely the predictive self-organizing fuzzy neural network architecture based on the fusion Adaptive Resonance Theory (fusion ART). Lastly, the problem of encoding, extracting and integrating multiple long-term, noisy continuous data, particularly from in-home spatiotemporal data, was studied. Specifically, the Episodic Memory Adaptive Resonance Theory (EM-ART) and SpatioTemporal Episodic Memory (STEM), which are variants of fusion ART, were employed to model movement trajectory and spatial time-series data of in-home room trips, respectively.
In summary, this thesis presents a review of the SOTA ML techniques and their predictive accuracy to detect MCI using digital biomarkers, followed by the development and validation of a suite of novel ML techniques, particularly involving the fusion ART, that can push the boundary of predictive accuracy for in-home detection of MCI in an ecologically valid environment. |
