Exploratory machine learning modeling of adaptive and maladaptive personality traits from passively sensed behavior

Runze Yan, Whitney R. Ringwald, Julio Vega, Madeline Kehl, Sang Won Bae, Anind K. Dey, Carissa A. Low, Aidan G.C. Wright, Afsaneh Doryab

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Continuous passive sensing of daily behavior from mobile devices has the potential to identify behavioral patterns associated with different aspects of human characteristics. This paper presents novel analytic approaches to extract and understand these behavioral patterns and their impact on predicting adaptive and maladaptive personality traits. Our machine learning analysis extends previous research by showing that both adaptive and maladaptive traits are associated with passively sensed behavior providing initial evidence for the utility of this type of data to study personality and its pathology. The analysis also suggests directions for future confirmatory studies into the underlying behavior patterns that link adaptive and maladaptive variants consistent with contemporary models of personality pathology.

Original languageEnglish (US)
Pages (from-to)266-281
Number of pages16
JournalFuture Generation Computer Systems
Volume132
DOIs
StatePublished - Jul 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

  • Behavior modeling
  • Data mining
  • Machine learning
  • Mobile and wearable sensing
  • Personality prediction

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