Facilitating CPAP adherence with personalized recommendations using artificial neural networks

Matheus Araujo, Tara Pereira, Jaideep Srivastava, Conrad Iber

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

Abstract

Sleep apnea is a common sleep disorder that, if left untreated, can have critical complications to the individual. The most common and effective treatment for sleep apnea is the Continuous Positive Airway Pressure (CPAP) therapy. But it has a long-term adherence rate as low as 60% due to discomfort and other factors. Although previous research has attempted to increase CPAP usage, there has been little to no change in its average adherence for the past two decades. This paper attempts to change this scenario using a large longitudinal dataset combined with a Recurrent Neural Network model to generate therapy use recommendations after one month of therapy. We performed a retrospective cohort analysis on 3380 patients during their first six months of therapy and compared our personalized recommendation system with the current generic recommendations made by sleep physicians. We show that recommendations generated by our artificial neural network model are easier to achieve since they are significantly closer to patients' therapy progress while being equally successful in maintaining therapy adherence.

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.
Pages97-100
Number of pages4
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

  • Recommendation Systems
  • Recurrent Neural Network
  • Sleep Apnea
  • Sleep Medicine
  • Therapy Adherence

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