Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
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
Show others and affiliations
2019 (English)In: Progress in Artificial Intelligence, ISSN 2192-6352, Vol. 8, no 4, p. 475-485Article in journal (Refereed) Published
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. Vol. 8, no 4, p. 475-485
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
National Category
Natural Sciences
Identifiers
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: 2020-01-10Bibliographically approved

Open Access in DiVA

fulltext(1220 kB)3 downloads
File information
File name FULLTEXT01.pdfFile size 1220 kBChecksum SHA-512
e09d20385ea794fe8846536fd44e72b6df146c62c03e44bf2f8f7666e886b72c4ef855584d686b88651b0f6ebc9bc08b4c639a8e304be33d0eab3017e8291cd9
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records BETA

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

Search in DiVA

By author/editor
Boman, MagnusBen Abdesslem, FehmiGillblad, DanielGörnerup, OlofSahlgren, Magnus
By organisation
SICS
Natural Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 3 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 26 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
v. 2.35.9