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Gated hidden markov models for early prediction of outcome of internet-based cognitive behavioral therapy
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
Department of Psychology, Faculty of Health and Life Sciences, Linnaeus University, Växjö, Sweden;Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, and Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden.
2019 (English)In: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Cham: Springer Verlag , 2019, p. 160-169Conference paper, Published paper (Refereed)
Abstract [en]

Depression is a major threat to public health and its mitigation is considered to be of utmost importance. Internet-based Cognitive Behavioral Therapy (ICBT) is one of the employed treatments for depression. However, for the approach to be effective, it is crucial that the outcome of the treatment is accurately predicted as early as possible, to allow for its adaptation to the individual patient. Hidden Markov models (HMMs) have been commonly applied to characterize systematic changes in multivariate time series within health care. However, they have limited capabilities in capturing long-range interactions between emitted symbols. For the task of analyzing ICBT data, one such long-range interaction concerns the dependence of state transition on fractional change of emitted symbols. Gated Hidden Markov Models (GHMMs) are proposed as a solution to this problem. They extend standard HMMs by modifying the Expectation Maximization algorithm; for each observation sequence, the new algorithm regulates the transition probability update based on the fractional change, as specified by domain knowledge. GHMMs are compared to standard HMMs and a recently proposed approach, Inertial Hidden Markov Models, on the task of early prediction of ICBT outcome for treating depression; the algorithms are evaluated on outcome prediction, up to 7 weeks before ICBT ends. GHMMs are shown to outperform both alternative models, with an improvement of AUC ranging from 12 to 23%. These promising results indicate that considering fractional change of the observation sequence when updating state transition probabilities may indeed have a positive effect on early prediction of ICBT outcome.

Place, publisher, year, edition, pages
Cham: Springer Verlag , 2019. p. 160-169
Series
Lecture Notes in Artificial Intelligence, ISSN 0302-9743, E-ISSN 1611-3349 ; 11526
Keywords [en]
Depression, Expectation maximization, Hidden Markov Models, Internet-based Cognitive Behavioral Therapy, Artificial intelligence, Forecasting, Health risks, Image segmentation, Maximum principle, Patient treatment, Cognitive-behavioral therapies, Expectation - maximizations, Expectation-maximization algorithms, Hidden markov models (HMMs), Multivariate time series, State transition probabilities, Transition probabilities
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:kth:diva-262465DOI: 10.1007/978-3-030-21642-9_22ISI: 000495606500022Scopus ID: 2-s2.0-85068339325ISBN: 9783030216412 (print)ISBN: 978-3-030-21642-9 (electronic)OAI: oai:DiVA.org:kth-262465DiVA, id: diva2:1361949
Conference
17th Conference on Artificial Intelligence in Medicine, AIME 2019; Poznan; Poland; 26-29 June 2019
Note

QC 20191017. QC 20191204

Available from: 2019-10-17 Created: 2019-10-17 Last updated: 2020-01-22Bibliographically approved

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Safinianaini, NegarBoström, Henrik

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