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Learning machines in Internet-delivered psychological treatment
RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.ORCID iD: 0000-0001-7949-1815
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0001-7866-143x
Karolinska Institute, Sweden; Stockholm County Council, Sweden.
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0001-8952-3542
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2019 (English)In: Progress in Artificial Intelligence, ISSN 2192-6352Article in journal (Refereed) Epub ahead of print
Abstract [en]

A learning machine, in the form of a gating network that governs a finite number of different machine learning methods, is described at the conceptual level with examples of concrete prediction subtasks. A historical data set with data from over 5000 patients in Internet-based psychological treatment will be used to equip healthcare staff with decision support for questions pertaining to ongoing and future cases in clinical care for depression, social anxiety, and panic disorder. The organizational knowledge graph is used to inform the weight adjustment of the gating network and for routing subtasks to the different methods employed locally for prediction. The result is an operational model for assisting therapists in their clinical work, about to be subjected to validation in a clinical trial.

Place, publisher, year, edition, pages
Springer Verlag , 2019.
Keywords [en]
Ensemble learning, Gating network, Internet-based psychological treatment, Learning machine, Machine learning, Decision support systems, Learning systems, Conceptual levels, Decision supports, Learning machines, Machine learning methods, Operational model, Organizational knowledge, Psychological treatments, Patient treatment
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Natural Sciences
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URN: urn:nbn:se:ri:diva-39062DOI: 10.1007/s13748-019-00192-0Scopus ID: 2-s2.0-85066625908OAI: oai:DiVA.org:ri-39062DiVA, id: diva2:1331045
Available from: 2019-06-26 Created: 2019-06-26 Last updated: 2019-07-31Bibliographically approved

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Boman, MagnusBen Abdesslem, FehmiGillblad, DanielGörnerup, OlofSahlgren, Magnus

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