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A Domains Oriented Framework of Recent Machine Learning Applications in Mobile Mental Health

In: Information Systems and Neuroscience

Author

Listed:
  • Max-Marcel Theilig

    (Technical University Berlin, Chair of Information and Communication Management)

  • Kim Janine Blankenhagel

    (Technical University Berlin, Chair of Information and Communication Management)

  • Rüdiger Zarnekow

    (Technical University Berlin, Chair of Information and Communication Management)

Abstract

This research illustrates how the interdisciplinary integration of mobile health (mHealth) and Machine Learning (ML) can contribute to implementing mobile care for mental health. 94 articles were identified in a literature review to derive functional domains and composing information items improving the comprehension of ML benefits with mHealth integration. Identified items of each domain were pooled into clusters and information flow was quantified according to prevailing occurrence of included articles. We derive a comprehensive domains oriented framework (DF) and visualize an information flow graph. The DF indicates that the utilization of ML is well established (e.g. stress detection, activity recognition). Because deployment and data acquisition currently relies heavily on mobile phones, only 65% of current applications make fully integrated use of data sources to assert patient’s mental state. Big data integration and a lack of commercially available devices to measure physiological or psychological parameters represent current bottlenecks to leverage synergies.

Suggested Citation

  • Max-Marcel Theilig & Kim Janine Blankenhagel & Rüdiger Zarnekow, 2019. "A Domains Oriented Framework of Recent Machine Learning Applications in Mobile Mental Health," Lecture Notes in Information Systems and Organization, in: Fred D. Davis & René Riedl & Jan vom Brocke & Pierre-Majorique Léger & Adriane B. Randolph (ed.), Information Systems and Neuroscience, pages 163-172, Springer.
    • Handle: RePEc:spr:lnichp:978-3-030-01087-4_20
    DOI: 10.1007/978-3-030-01087-4_20
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