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Digital phenotype of mood disorders: A conceptual and critical review
16 references·0 citing·53 second hopsampled
Digital phenotype of mood disorders: A conceptual and critical review (2022)Digital phenotype of mood disorders: A co…Research Domain Criteria (RDoC): Progress and Potential (2022)Research Domain Criteria (RDo…Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study (2021)Predicting Depression From Sm…Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study (2021)Evaluation of Changes in Depr…Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling (2021)Digital Biomarkers for Depres…Detection of Minor and Major Depression through Voice as a Biomarker Using Machine Learning (2021)Detection of Minor and Major …Automatic detection of depression symptoms in twitter using multimodal analysis (2021)Automatic detection of depres…STDD: Short-Term Depression Detection with Passive Sensing (2020)STDD: Short-Term Depression D…Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones (2020)Passive Sensing of Prediction…Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020)Monitoring Changes in Depress…Using Digital Phenotyping to Accurately Detect Depression Severity (2019)Using Digital Phenotyping to …Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study (2019)Mood Prediction of Patients W…Digital biomarkers of mood disorders and symptom change (2019)Digital biomarkers of mood di…Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study (2018)Predicting Mood Disturbance S…Depression detection from social network data using machine learning techniques (2018)Depression detection from soc…Automatic detection of major depressive disorder using electrodermal activity (2018)Automatic detection of major …New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices (2017)New dimensions and new tools …Diagnostic and Statistical Manual of Mental Disorders (2013)Toward the future of psychiatric diagnosis: the seven pillars of RDoC (2013)The NIMH Research Domain Criteria (RDoC) Project: Precision Medicine for Psychiatry (2014)Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia (2018)Digital phenotyping of suicidal thoughts (2018)Assessment of the Data Sharing and Privacy Practices of Smartphone Apps for Depression and Smoking Cessation (2019)A RATING SCALE FOR DEPRESSION (1960)Ecological Momentary Assessment (2007)Estimating the true global burden of mental illness (2016)Detecting relapse in youth with psychotic disorders utilizing patient-generated and patient-contributed digital data from Facebook (2019)Touchscreen typing pattern analysis for remote detection of the depressive tendency (2019)Qualitative Data Analysis with NVivo (2007)The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods (2009)Qualitative data analysis with NVIVO (2014)Deep learning for misinformation detection on online social networks: a survey and new perspectives (2020)Natural language processing applied to mental illness detection: a narrative review (2022)RETRACTED: Deep Learning for Depression Detection from Textual Data (2022)The global burden of mental disorders: An update from the WHO World Mental Health (WMH) Surveys (2009)Method matters: Understanding diagnostic reliability in DSM-IV and DSM-5. 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References

  • 2018Depression detection from social network data using machine learning techniques465 cited
  • 2017New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices348 cited
  • 2018Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study233 cited
  • 2022Research Domain Criteria (RDoC): Progress and Potential195 cited
  • 2019Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study183 cited
  • 2019Digital biomarkers of mood disorders and symptom change160 cited
  • 2020Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors158 cited
  • 2021Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling151 cited
  • 2021Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study143 cited
  • 2020Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones139 cited
  • 2021Automatic detection of depression symptoms in twitter using multimodal analysis122 cited
  • 2020STDD: Short-Term Depression Detection with Passive Sensing109 cited
  • 2019Using Digital Phenotyping to Accurately Detect Depression Severity

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94 cited
  • 2021Detection of Minor and Major Depression through Voice as a Biomarker Using Machine Learning94 cited
  • 2018Automatic detection of major depressive disorder using electrodermal activity83 cited
  • 2021Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study82 cited
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