Learning sleep quality from daily logs

Sungkyu Park, Cheng Hsu, Cheng Te Li, Sang Won Lee, Sungwon Han, Meeyoung Cha

研究成果: Conference contribution

5 引文 斯高帕斯(Scopus)

摘要

Precision psychiatry is a new research field that uses advanced data mining over a wide range of neural, behavioral, psychological, and physiological data sources for classification of mental health conditions. This study presents a computational framework for predicting sleep efficiency of insomnia sufferers. A smart band experiment is conducted to collect heterogeneous data, including sleep records, daily activities, and demographics, whose missing values are imputed via Improved Generative Adversarial Imputation Networks (Imp-GAIN). Equipped with the imputed data, we predict sleep efficiency of individual users with a proposed interpretable LSTM-Attention (LA Block) neural network model. We also propose a model, Pairwise Learning-based Ranking Generation (PLRG), to rank users with high insomnia potential in the next day. We discuss implications of our findings from the perspective of a psychiatric practitioner. Our computational framework can be used for other applications that analyze and handle noisy and incomplete time-series human activity data in the domain of precision psychiatry.

原文English
主出版物標題KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
發行者Association for Computing Machinery
頁面2421-2429
頁數9
ISBN(電子)9781450362016
DOIs
出版狀態Published - 2019 七月 25
事件25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019 - Anchorage, United States
持續時間: 2019 八月 42019 八月 8

出版系列

名字Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
國家/地區United States
城市Anchorage
期間19-08-0419-08-08

All Science Journal Classification (ASJC) codes

  • 軟體
  • 資訊系統

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