Defining and monitoring patient clusters based on therapy adherence in sleep apnea management

Mourya Karan Reddy Baddam, Matheus Araujo, Jaideep Srivastava

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Obstructive Sleep Apnea (OSA) is a disorder in which breathing repeatedly stops and starts due to recurrent episodes of partial and complete airway obstruction during sleep. One of the common treatments for moderate and severe OSA cases is the use of Continuous Positive Airway Pressure (CPAP) devices that keep the airways open. Unfortunately, about 40% of the patients using CPAP devices abandon their therapy within six months. In this work, we propose a method to cluster and monitor patients according to their therapy usage behavior aiming for a timely and appropriate intervention. Our data corresponds to 1815 CPAP users in their first six months of therapy. In contrast to the simple rule-based methods currently employed by sleep clinics to identify non-adherent behavior, our approach uses clustering techniques to group patients based on their CPAP usage patterns. After identifying four main clusters, we investigate how patients can change between clusters over the months using Markov Chain analysis. We observed that patients who change to a healthy cluster have a higher probability of staying there in the future, reinforcing the need for early intervention. Finally, we use machine learning-based models to predict the next month's probability of adherence and nonadherence according to our pre-defined cluster definitions.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE 34th International Symposium on Computer-Based Medical Systems, CBMS 2021
EditorsJoao Rafael Almeida, Alejandro Rodriguez Gonzalez, Linlin Shen, Bridget Kane, Agma Traina, Paolo Soda, Jose Luis Oliveira
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages580-585
Number of pages6
ISBN (Electronic)9781665441216
DOIs
StatePublished - Jun 1 2021
Event34th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2021 - Virtual, Online
Duration: Jun 7 2021Jun 9 2021

Publication series

Name2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)

Conference

Conference34th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2021
CityVirtual, Online
Period6/7/216/9/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Clustering
  • Machine Learning
  • Markov Chain
  • Obstructive Sleep Apnea
  • Random Forest
  • SVM
  • Sleep Therapy
  • Therapy Adherence
  • XGBoost

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