Contents

Learning Sleep Quality from Daily Logs

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

  • 528 ITEM VIEW
  • 0 DOWNLOAD
Abstract

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.

Issue Date
2019-08
Publisher
Association for Computing Machinery
URI
https://archives.kdischool.ac.kr/handle/11125/54977
10.1145/3292500.3330792
Conf. Name
25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Place
US
Conference Date
2019-08-04
Files in This Item:
    There are no files associated with this item.

Click the button and follow the links to connect to the full text. (KDI CL members only)

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

상단으로 이동