The accuracy of passive phone sensors in predicting daily mood

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

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The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit

(2017) Yu-Feng Yvonne Chan et al.   NATURE BIOTECHNOLOGY

The need to approximate the use-case in clinical machine learning

(2017) Sohrab Saeb et al.   GigaScience

Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety

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Feasibility of Obtaining Measures of Lifestyle From a Smartphone App

(2017) Michael V. McConnell et al.   JAMA Cardiology

Harnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health

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Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild

(2016) Fabian Wahle et al.   JMIR mHealth and uHealth

Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study

(2015) Sohrab Saeb et al.   JOURNAL OF MEDICAL INTERNET RESEARCH

Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010

(2013) Harvey A Whiteford et al.   LANCET

Does Response on the PHQ-9 Depression Questionnaire Predict Subsequent Suicide Attempt or Suicide Death?

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Random forests for genomic data analysis

(2012) Xi Chen et al.   GENOMICS

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

(2012) Anne-Laure Boulesteix et al.   Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery

Harnessing Context Sensing to Develop a Mobile Intervention for Depression

(2011) Michelle Nicole Burns et al.   JOURNAL OF MEDICAL INTERNET RESEARCH