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Learning Sleep Quality from Daily Logs

Published: 25 July 2019 Publication History

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.

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  • (2024)Flow and Physiological Response Assessment during Exercise Using Metrorhythmic StimuliJournal of Human Kinetics10.5114/jhk/187804Online publication date: 17-Jul-2024
  • (2024)Context-Aware Behavioral Tips to Improve Sleep Quality via Machine Learning and Large Language ModelsFuture Internet10.3390/fi1602004616:2(46)Online publication date: 30-Jan-2024
  • (2024)An Ensemble Classification Model for Depression Based on Wearable Device Sleep DataIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.325860128:5(2602-2612)Online publication date: May-2024
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    cover image ACM Conferences
    KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
    July 2019
    3305 pages
    ISBN:9781450362016
    DOI:10.1145/3292500
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 25 July 2019

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    Author Tags

    1. data imputation
    2. insomnia
    3. interpretability
    4. precision psychiatry
    5. ranking model
    6. time-series data

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    KDD '19 Paper Acceptance Rate 110 of 1,200 submissions, 9%;
    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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    Cited By

    View all
    • (2024)Flow and Physiological Response Assessment during Exercise Using Metrorhythmic StimuliJournal of Human Kinetics10.5114/jhk/187804Online publication date: 17-Jul-2024
    • (2024)Context-Aware Behavioral Tips to Improve Sleep Quality via Machine Learning and Large Language ModelsFuture Internet10.3390/fi1602004616:2(46)Online publication date: 30-Jan-2024
    • (2024)An Ensemble Classification Model for Depression Based on Wearable Device Sleep DataIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2023.325860128:5(2602-2612)Online publication date: May-2024
    • (2024)An Imputation Approach to Electronic Medical Records Based on Time Series and Feature Association12th Asian-Pacific Conference on Medical and Biological Engineering10.1007/978-3-031-51485-2_28(259-276)Online publication date: 26-Feb-2024
    • (2023)Missing Value Imputation Methods for Electronic Health RecordsIEEE Access10.1109/ACCESS.2023.325191911(21562-21574)Online publication date: 2023
    • (2023)Social dimensions impact individual sleep quantity and qualityScientific Reports10.1038/s41598-023-36762-513:1Online publication date: 15-Jun-2023
    • (2022)Monitoring and Improving Personalized Sleep Quality from Long-Term Lifelogs2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020829(4356-4364)Online publication date: 17-Dec-2022
    • (2021)Affective State during Physiotherapy and Its Analysis Using Machine Learning MethodsSensors10.3390/s2114485321:14(4853)Online publication date: 16-Jul-2021
    • (2020)HealthWalksProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34322294:4(1-26)Online publication date: 18-Dec-2020
    • (2020)Processing of Healthcare Data to Investigate the Correlations and the Anomalies2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)10.1109/I-SMAC49090.2020.9243400(611-617)Online publication date: 7-Oct-2020

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