Personalized versus generic mood prediction models in bipolar disorder

Marios Constantinides, Jonas Busk, Aleksandar Matic, Maria Faurholt-Jepsen, Lars Vedel Kessing, Jakob E. Bardram

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

A number of studies have been investigating the use of mobile phone sensing to predict mood in unipolar (depression) and bipolar disorder. However, most of these studies included a small number of people making it difficult to understand the feasibility of this method in practice. This paper reports on mood prediction from a large (N=129) sample of bipolar disorder patients. We achieved prediction accuracies of 89% and 58% in personalized and generic models respectively. Moreover, we shed light on the "cold-start" problem in practice and we show that the accuracy depends on the labeling strategy of euthymic states. The paper discusses the results, the difference between personalized and generic models, and the use of mobile phones in mental health treatment in practice.

Original languageEnglish
Title of host publicationProceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers
PublisherAssociation for Computing Machinery
Publication date8 Oct 2018
Pages1700-1707
ISBN (Electronic)9781450359665
DOIs
Publication statusPublished - 8 Oct 2018
Event2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable Computers, ISWC 2018 - Singapore, Singapore
Duration: 8 Oct 201812 Oct 2018
http://ubicomp.org/ubicomp2018/

Conference

Conference2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable Computers, ISWC 2018
CountrySingapore
CitySingapore
Period08/10/201812/10/2018
SponsorAssociation for Computing Machinery
Internet address

Keywords

  • Bipolar Disorder
  • Depression
  • Generic Models
  • Mobile Sensing
  • Personalized

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