Stein2022 - Psychiatric diagnosis and treatment in the 21st century, paradigm shifts versus incremental integration

  • Type:#article
  • Date read: 2022-09-13
  • Subject:
  • Bibtex: @stein2022
  • Bibliography: Stein, D. J., Shoptaw, S. J., Vigo, D. V., Lund, C., Cuijpers, P., Bantjes, J., Sartorius, N., & Maj, M. (2022). Psychiatric diagnosis and treatment in the 21st century: Paradigm shifts versus incremental integration. World Psychiatry, 21(3), 393–414.

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Key takeaways

Deinstitutionalization and community mental health care

Digital phenotyping and digital therapies

  • CCTV images to identify suicide attempts in progress at hotspots (ref 160)
  • Refs 81-83 seem interesting
    • Dwyer DB, Falkai P, Koutsouleris N. Machine learning approaches for clinical psychology and psychiatry. Annu Rev Clin Psychol 2018;14:91­ 118.
    • Rutledge RB, Chekroud AM, Huys QJ. Machine learning and big data in psychiatry: toward clin­ ical applications. Curr Opin Neurobiol 2019;55: 152­9.
    • Koppe G, Meyer­Lindenberg A, Durstewitz D. Deep learning for small and big data in psychia­ try. Neuropsychopharmacology 2021;46:176­90.
    • Checkroud AM, Bondar J, Delgadillo J et al. The promise of machine learning in predicting treat­ ment outcomes in psychiatry. World Psychiatry 2021;20:154­93.

The ability to access, store and manipulate data, together with the use of machine learning algorithms, promises to advance the practice of individualized medicine in psychiatry by allowing matching of patients with the most appropriate therapies

  • Active data collection vs passive data collection
    • Passive data collection can be used for digital phenotyping
  • Limitations in app-based trt (beyond security issues and lack of evidence-base)
    • High data costs
    • Unstable Internet connections
    • Bandwidth limitations