Fine tuned personalized machine learning models to detect insomnia risk based on data from a smart bed platform

Trevor Winger; Vidhya Chellamuthu; Dmytro Guzenko; Mark Aloia; Shawn Barr; Susan DeFranco; Brandon Gorski; Faisal Mushtaq; Gary Garcia-Molina

doi:10.3389/fneur.2024.1303978

Fine tuned personalized machine learning models to detect insomnia risk based on data from a smart bed platform

Front Neurol. 2024 Feb 14:15:1303978. doi: 10.3389/fneur.2024.1303978. eCollection 2024.

Authors

Trevor Winger^{1

2}, Vidhya Chellamuthu¹, Dmytro Guzenko³, Mark Aloia^{4

5}, Shawn Barr¹, Susan DeFranco⁴, Brandon Gorski⁴, Faisal Mushtaq¹, Gary Garcia-Molina^{1

6}

Affiliations

¹ Sleep Number Labs, Sleep Number, San Jose, CA, United States.
² Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, United States.
³ GlobalLogic, Kyiv, Ukraine.
⁴ Sleep Number Corporation, Minneapolis, MN, United States.
⁵ National Jewish Health, Denver, CO, United States.
⁶ Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States.

Abstract

Introduction: Insomnia causes serious adverse health effects and is estimated to affect 10-30% of the worldwide population. This study leverages personalized fine-tuned machine learning algorithms to detect insomnia risk based on questionnaire and longitudinal objective sleep data collected by a smart bed platform.

Methods: Users of the Sleep Number smart bed were invited to participate in an IRB approved study which required them to respond to four questionnaires (which included the Insomnia Severity Index; ISI) administered 6 weeks apart from each other in the period from November 2021 to March 2022. For 1,489 participants who completed at least 3 questionnaires, objective data (which includes sleep/wake and cardio-respiratory metrics) collected by the platform were queried for analysis. An incremental, passive-aggressive machine learning model was used to detect insomnia risk which was defined by the ISI exceeding a given threshold. Three ISI thresholds (8, 10, and 15) were considered. The incremental model is advantageous because it allows personalized fine-tuning by adding individual training data to a generic model.

Results: The generic model, without personalizing, resulted in an area under the receiving-operating curve (AUC) of about 0.5 for each ISI threshold. The personalized fine-tuning with the data of just five sleep sessions from the individual for whom the model is being personalized resulted in AUCs exceeding 0.8 for all ISI thresholds. Interestingly, no further AUC enhancements resulted by adding personalized data exceeding ten sessions.

Discussion: These are encouraging results motivating further investigation into the application of personalized fine tuning machine learning to detect insomnia risk based on longitudinal sleep data and the extension of this paradigm to sleep medicine.

Keywords: fine tuning; incremental learning; insomnia risk; passive-aggressive learning; personalized machine learning.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was funded by Sleep Number Corporation.