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Predicting the 9-year course of mood and anxiety disorders with automated machine learning: A comparison between auto-sklearn, naïve Bayes classifier, and traditional logistic regression.
van Eeden, Wessel A; Luo, Chuan; van Hemert, Albert M; Carlier, Ingrid V E; Penninx, Brenda W; Wardenaar, Klaas J; Hoos, Holger; Giltay, Erik J.
Affiliation
  • van Eeden WA; Department of Psychiatry, Leiden University Medical Center, Leiden, the Netherlands. Electronic address: W.A.van_Eeden@lumc.nl.
  • Luo C; Leiden Institute of Advanced Computer Sciences, Leiden University, Leiden, the Netherlands.
  • van Hemert AM; Department of Psychiatry, Leiden University Medical Center, Leiden, the Netherlands.
  • Carlier IVE; Department of Psychiatry, Leiden University Medical Center, Leiden, the Netherlands.
  • Penninx BW; Department of Psychiatry, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, VU University Medical Center, and GGZ inGeest, Amsterdam, the Netherlands.
  • Wardenaar KJ; Department of Psychiatry, The University Medical Center Groningen, Groningen, the Netherlands.
  • Hoos H; Leiden Institute of Advanced Computer Sciences, Leiden University, Leiden, the Netherlands.
  • Giltay EJ; Department of Psychiatry, Leiden University Medical Center, Leiden, the Netherlands.
Psychiatry Res ; 299: 113823, 2021 05.
Article in En | MEDLINE | ID: mdl-33667949
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Anxiety Disorders / Machine Learning Type of study: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Aspects: Patient_preference Limits: Adult / Female / Humans Language: En Journal: Psychiatry Res Year: 2021 Document type: Article Country of publication: Ireland
Fulltext
Add to My VHL
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Anxiety Disorders / Machine Learning Type of study: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Aspects: Patient_preference Limits: Adult / Female / Humans Language: En Journal: Psychiatry Res Year: 2021 Document type: Article Country of publication: Ireland