Learning machines in Internet-delivered psychological treatment

被引:0
作者
Magnus Boman
Fehmi Ben Abdesslem
Erik Forsell
Daniel Gillblad
Olof Görnerup
Nils Isacsson
Magnus Sahlgren
Viktor Kaldo
机构
[1] KTH/EECS/SCS/MCS,Department of Clinical Neuroscience, Centre for Psychiatry Research
[2] RISE,Department of Psychology, Faculty of Health and Life Sciences
[3] Karolinska Institutet and Stockholm Health Care Services,undefined
[4] Linnaeus University,undefined
来源
Progress in Artificial Intelligence | 2019年 / 8卷
关键词
Learning machine; Machine learning; Ensemble learning; Gating network; Internet-based psychological treatment;
D O I
暂无
中图分类号
学科分类号
摘要
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.
引用
收藏
页码:475 / 485
页数:10
相关论文
共 50 条
  • [31] An Incremental Learning Approach for Restricted Boltzmann Machines
    Yu, Jongmin
    Gwak, Jeonghwan
    Lee, Sejeong
    Jeon, Moongu
    FOURTH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND INFORMATION SCIENCES (CCAIS 2015), 2015, : 113 - 117
  • [32] Semi-supervised learning with graph convolutional extreme learning machines
    Zhang, Zijia
    Cai, Yaoming
    Gong, Wenyin
    EXPERT SYSTEMS WITH APPLICATIONS, 2023, 213
  • [33] Analytics, Machine Learning, and the Internet of Things
    Earley, Seth
    IT PROFESSIONAL, 2015, 17 (01) : 10 - 13
  • [34] Deep Learning of Markov Model-Based Machines for Determination of Better Treatment Option Decisions for Infertile Women
    Arni S.R. Srinivasa Rao
    Michael P. Diamond
    Reproductive Sciences, 2020, 27 : 763 - 770
  • [35] Deep Learning of Markov Model-Based Machines for Determination of Better Treatment Option Decisions for Infertile Women
    Rao, Arni S. R. Srinivasa
    Diamond, Michael P.
    REPRODUCTIVE SCIENCES, 2020, 27 (02) : 763 - 770
  • [36] Improve quality of service for the Internet of Things using Blockchain & machine learning algorithms
    Chesuh, Lawrence Nforh
    Fernandez-Diaz, Ramon Angel
    Alija-Perez, Jose Manuel
    Benavides-Cuellar, Carmen
    Alaiz-Moreton, Hector
    INTERNET OF THINGS, 2024, 26
  • [37] Machine Learning in Psychometrics and Psychological Research
    Orru, Graziella
    Monaro, Merylin
    Conversano, Ciro
    Gemignani, Angelo
    Sartori, Giuseppe
    FRONTIERS IN PSYCHOLOGY, 2020, 10
  • [38] A psychological approach to learning causal networks
    Manaf Zargoush
    Farrokh Alemi
    Vinzenzo Esposito Vinzi
    Jee Vang
    Raya Kheirbek
    Health Care Management Science, 2014, 17 : 194 - 201
  • [39] A psychological approach to learning causal networks
    Zargoush, Manaf
    Alemi, Farrokh
    Vinzi, Vinzenzo Esposito
    Vang, Jee
    Kheirbek, Raya
    HEALTH CARE MANAGEMENT SCIENCE, 2014, 17 (02) : 194 - 201
  • [40] Online Sequential Learning based on Extreme Learning Machines for Particulate Matter Forecasting
    Bueno, Andres
    Coelho, Guilherme Palermo
    Bertini, Joao Roberto, Jr.
    2017 6TH BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), 2017, : 169 - 174