A survey on big data-driven digital phenotyping of mental health

Yunji Liang, Xiaolong Zheng, Daniel D. Zeng

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

The landscape of mental health has undergone tremendous changes within the last two decades, but the research on mental health is still at the initial stage with substantial knowledge gaps and the lack of precise diagnosis. Nowadays, big data and artificial intelligence offer new opportunities for the screening and prediction of mental problems. In this review paper, we outline the vision of digital phenotyping of mental health (DPMH) by fusing the enriched data from ubiquitous sensors, social media and healthcare systems, and present a broad overview of DPMH from sensing and computing perspectives. We first conduct a systematical literature review and propose the research framework, which highlights the key aspects related with mental health, and discuss the challenges elicited by the enriched data for digital phenotyping. Next, five key research strands including affect recognition, cognitive analytics, behavioral anomaly detection, social analytics, and biomarker analytics are unfolded in the psychiatric context. Finally, we discuss various open issues and the corresponding solutions to underpin the digital phenotyping of mental health.

Original languageEnglish (US)
Pages (from-to)290-307
Number of pages18
JournalInformation Fusion
Volume52
DOIs
StatePublished - Dec 2019

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Keywords

  • Big data
  • Data mining
  • Digital phenotyping
  • Information fusion
  • Mental health

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems
  • Hardware and Architecture

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