Generating natural language summaries from temporal personal health data

Authors
Harris, Jonathan
ORCID
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Other Contributors
Hendler, Jim
Bennett, Kristin, P.
Chen, Ching-Hua
Zaki, Mohammed, J.
Issue Date
2022-12
Keywords
Computer science
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute (RPI), Troy, NY. Copyright of original work retained by author.
Full Citation
URI
https://hdl.handle.net/20.500.13015/6348
Abstract
Within the personal health domain, there is a vast amount of temporal knowledge that can be collected about an individual (e.g., their time-stamped heart rate and step count data) due to the recent surge in the production of health and fitness tracking devices (i.e., smart, wearable technologies such as smart watches). Although everyday individuals have access to their own data via their personal mobile devices, they typically lack the knowledge or tools required to access the underlying patterns hidden within. Without these patterns, there is a sea of information unavailable to them that could potentially aid them in better comprehending their data and utilizing this knowledge to improve their daily routines. The work within this dissertation focuses on closing this gap between the non-expert individual and the meaningful patterns within their temporal personal health data. The basis of this work revolves around the incorporation of automated explainability in the form of natural language summary generation to highlight the behaviors exhibited by an individual and to evaluate them against their defined health goals. In particular, this work showcases the contributions of our time-series summarization framework within the personal health domain, automating this approach via deep learning, and future plans to include unsupervised rule generation.
Description
December 2022
School of Science
Department
Dept. of Computer Science
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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Users may download and share copies with attribution in accordance with a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 license. No commercial use or derivatives are permitted without the explicit approval of the author.
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