Predicting short term mood developments among depressed patients using adherence and ecological momentary assessment data.

Mikus, Adam; Hoogendoorn, Mark; Rocha, Artur; Gama, Joao; Ruwaard, Jeroen; Riper, Heleen

Mikus, Adam; Hoogendoorn, Mark; Rocha, Artur; Gama, Joao; Ruwaard, Jeroen; Riper, Heleen.

Affiliation

Mikus A; Vrije Universiteit Amsterdam, Department of Computer Science, De Boelelaan 1081, Amsterdam 1081 HV, The Netherlands.
Hoogendoorn M; Vrije Universiteit Amsterdam, Department of Computer Science, De Boelelaan 1081, Amsterdam 1081 HV, The Netherlands.
Rocha A; Centre for Information Systems and Computer Graphics, INESC TEC, Porto, Portugal.
Gama J; University of Porto, Laboratory of Artificial Intelligence and Decision Support, Porto, Portugal.
Ruwaard J; Vrije Universiteit Amsterdam, Department of Clinical Psychology, De Boelelaan 1081, Amsterdam 1081 HV, The Netherlands.
Riper H; Vrije Universiteit Amsterdam, Department of Clinical Psychology, De Boelelaan 1081, Amsterdam 1081 HV, The Netherlands.

Internet Interv ; 12: 105-110, 2018 Jun.

Article de En | MEDLINE | ID: mdl-30135774

Mots clés

Depression; Machine learning; Prediction; Short term mood

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Prognostic_studies / Risk_factors_studies Langue: En Journal: Internet Interv Année: 2018 Type de document: Article Pays d'affiliation: Pays-Bas Pays de publication: Pays-Bas

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