TY - GEN
T1 - Learning sleep quality from daily logs
AU - Park, Sungkyu
AU - Hsu, Cheng
AU - Li, Cheng Te
AU - Lee, Sang Won
AU - Han, Sungwon
AU - Cha, Meeyoung
N1 - Funding Information:
We thank Sungwon Park for his contribution on evaluating baseline models. This research was supported by Basic Science Research Program (No. NRF-2017R1E1A1A01076400) and Next-Generation Information Computing Development Program (No. NRF-2017M3C4A7063570) through the National Research Foundation of Korea funded by the Ministry of Science and ICT in Korea. This work was also supported by Ministry of Science and Technology (MOST) Taiwan with grants 108-2636-E-006-002 (MOST Young Scholar Fellowship Program) and 107-2218-E-006-040, and supported by Academia Sinica Thematic Research Program with grant AS-107-TP-M05.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/7/25
Y1 - 2019/7/25
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85071148818&partnerID=8YFLogxK
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U2 - 10.1145/3292500.3330792
DO - 10.1145/3292500.3330792
M3 - Conference contribution
AN - SCOPUS:85071148818
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 2421
EP - 2429
BT - KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
Y2 - 4 August 2019 through 8 August 2019
ER -