Depression [NEW TOPIC PAGE]

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Last Posted: Jun 23, 2020

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• Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial.
  Rajpurkar Pranav et al. JAMA network open 2020 Jun 3(6) e206653
• Combinatorial PharmacogenomicTesting Improves Outcomes for Older Adults With Depression.
  Forester Brent P et al. The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry 2020 May
• Implementation and evaluation of a nurse-led decision-coaching program for healthy breast cancer susceptibility gene (BRCA1/2) mutation carriers: a study protocol for the randomized controlled EDCP-BRCA study.
  Isselhard A et al. Trials 2020 Jun 21(1) 501
• Male-female differences in quality of life and coping style in patients with Marfan syndrome and hereditary thoracic aortic diseases.
  Thijssen Carlijn G E et al. Journal of genetic counseling 2020 Jun
• Predictors of Fabry disease in patients with hypertrophic cardiomyopathy: How to guide the diagnostic strategy?
  Azevedo Olga et al. American heart journal 2020 Apr 226114-126
• A pragmatic randomized waitlist-controlled effectiveness and cost-effectiveness trial of digital interventions for depression and anxiety
  D Richards et al, NPJ Digital Medicine, June 15, 2020
• Grant Report on SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics.
  Kamath Jayesh et al. Journal of psychiatry and brain science 2020 5
• Predicting the naturalistic course in anxiety disorders using clinical and biological markers: a machine learning approach.
  Bokma Wicher A et al. Psychological medicine 2020 Jun 1-11
• Combinatorial Pharmacogenomic Algorithm is Predictive of Citalopram and Escitalopram Metabolism in Patients with Major Depressive Disorder.
  Shelton Richard C et al. Psychiatry research 2020 May 290113017
• A machine learning approach predicts future risk to suicidal ideation from social media data.
  Roy Arunima et al. NPJ digital medicine 2020 378

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