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Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review

World neurosurgery, 2018-01, Vol.109, p.476-486.e1

2018

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Incorporating Contact Network Structure in Cluster Randomized Trials

Scientific reports, 2015-12, Vol.5 (1), p.17581-17581, Article 17581

2015

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Design and results of a smartphone‐based digital phenotyping study to quantify ALS progression

Annals of clinical and translational neurology, 2019-05, Vol.6 (5), p.873-881

2019

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Estimating longitudinal depressive symptoms from smartphone data in a transdiagnostic cohort

Brain and behavior, 2022-02, Vol.12 (2), p.e02077-n/a

2022

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Barriers, Benefits, and Beliefs of Brain Training Smartphone Apps: An Internet Survey of Younger US Consumers

Frontiers in human neuroscience, 2016-04, Vol.10, p.180-180

2016

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Relapse prediction in schizophrenia through digital phenotyping: a pilot study

Neuropsychopharmacology (New York, N.Y.), 2018-07, Vol.43 (8), p.1660-1666

2018

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Realizing the Potential of Mobile Mental Health: New Methods for New Data in Psychiatry

Current psychiatry reports, 2015-08, Vol.17 (8), p.602-602, Article 61

2015

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Beyond smartphones and sensors: choosing appropriate statistical methods for the analysis of longitudinal data

Journal of the American Medical Informatics Association : JAMIA, 2018-12, Vol.25 (12), p.1669-1674

2018

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An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning

Neurosurgery, 2020-02, Vol.86 (2), p.E184-E192

2020

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Utilizing a Personal Smartphone Custom App to Assess the Patient Health Questionnaire-9 (PHQ-9) Depressive Symptoms in Patients With Major Depressive Disorder

JMIR mental health, 2015-01, Vol.2 (1), p.e8-e8

2015

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Leveraging Smartphone-Based Data Collection to Map Homogenous Longitudinal Trajectory Subgroups of Self-Report Symptoms in the Early Aftermath of Trauma

Biological psychiatry (1969), 2020-05, Vol.87 (9), p.S75

2020

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151. Relapse Prediction in Schizophrenia through Digital Phenotyping

Biological psychiatry (1969), 2018-05, Vol.83 (9), p.S61-S62

2018

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149. Characterizing the Clinical Relevance of Digital Phenotyping Data Quality With Applications to a Cohort With Schizophrenia

Biological psychiatry (1969), 2018-05, Vol.83 (9), p.S61-S61

2018

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150. Automated Longitudinal Latent Interval Estimation With Applications to Sleep

Biological psychiatry (1969), 2018-05, Vol.83 (9), p.S61-S61

2018

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Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia

NPJ digital medicine, 2018-04, Vol.1 (1), p.15-15, Article 15

2018

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HOUT-20. AN ONLINE CALCULATOR FOR THE PREDICTION OF SURVIVAL AND ADJUVANT TREATMENT BENEFIT IN GLIOBLASTOMA PATIENTS

Neuro-oncology (Charlottesville, Va.), 2018-11, Vol.20 (suppl_6), p.vi117-vi117

2018

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369. Digital Phenotyping in Schizophrenia

Biological psychiatry (1969), 2017-05, Vol.81 (10), p.S151-S151

2017

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323. Utilizing Smartphones to Collect Longitudinal Digital Phenotypes in Patients with Schizophrenia

Biological psychiatry (1969), 2017-05, Vol.81 (10), p.S132-S133

2017

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The HOPE Pilot Study: Harnessing Patient-Reported Outcomes and Biometric Data to Enhance Cancer Care

JCO clinical cancer informatics, 2018-12, Vol.2 (2), p.1-12

2018

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Digital Phenotyping in Schizophrenia

Biological psychiatry (1969), 2017, Vol.81 (10), p.S151-S151

2017

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Utilizing Smartphones to Collect Longitudinal Digital Phenotypes in Patients with Schizophrenia

Biological psychiatry (1969), 2017, Vol.81 (10), p.S132-S133

2017

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M48. Digital Phenotyping in Schizophrenia Using Smartphones

Schizophrenia bulletin, 2017-03, Vol.43 (suppl_1), p.S228-S228

2017

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Corrigendum: Barriers, Benefits, and Beliefs of Brain Training Smartphone Apps: An Internet Survey of Younger US Consumers

Frontiers in human neuroscience, 2016-06, Vol.10, p.253-253

2016

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QLIF-18. DIGITAL PHENOTYPING: A NOVEL METHOD FOR QUALITY OF LIFE ASSESSMENT IN PATIENTS WITH BRAIN TUMORS

Neuro-oncology (Charlottesville, Va.), 2017-11, Vol.19 (suppl_6), p.vi205-vi205

2017

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Using a network-based approach and targeted maximum likelihood estimation to evaluate the effect of adding pre-exposure prophylaxis to an ongoing test-and-treat trial

Clinical trials (London, England), 2017-04, Vol.14 (2), p.201-210

2017

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Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review

Incorporating Contact Network Structure in Cluster Randomized Trials

Design and results of a smartphone‐based digital phenotyping study to quantify ALS progression

Estimating longitudinal depressive symptoms from smartphone data in a transdiagnostic cohort

Barriers, Benefits, and Beliefs of Brain Training Smartphone Apps: An Internet Survey of Younger US Consumers

Relapse prediction in schizophrenia through digital phenotyping: a pilot study

Realizing the Potential of Mobile Mental Health: New Methods for New Data in Psychiatry

Beyond smartphones and sensors: choosing appropriate statistical methods for the analysis of longitudinal data

An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning

Utilizing a Personal Smartphone Custom App to Assess the Patient Health Questionnaire-9 (PHQ-9) Depressive Symptoms in Patients With Major Depressive Disorder

Leveraging Smartphone-Based Data Collection to Map Homogenous Longitudinal Trajectory Subgroups of Self-Report Symptoms in the Early Aftermath of Trauma

151. Relapse Prediction in Schizophrenia through Digital Phenotyping

149. Characterizing the Clinical Relevance of Digital Phenotyping Data Quality With Applications to a Cohort With Schizophrenia

150. Automated Longitudinal Latent Interval Estimation With Applications to Sleep

Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia

HOUT-20. AN ONLINE CALCULATOR FOR THE PREDICTION OF SURVIVAL AND ADJUVANT TREATMENT BENEFIT IN GLIOBLASTOMA PATIENTS

369. Digital Phenotyping in Schizophrenia

323. Utilizing Smartphones to Collect Longitudinal Digital Phenotypes in Patients with Schizophrenia

The HOPE Pilot Study: Harnessing Patient-Reported Outcomes and Biometric Data to Enhance Cancer Care

Digital Phenotyping in Schizophrenia

Utilizing Smartphones to Collect Longitudinal Digital Phenotypes in Patients with Schizophrenia

M48. Digital Phenotyping in Schizophrenia Using Smartphones

Corrigendum: Barriers, Benefits, and Beliefs of Brain Training Smartphone Apps: An Internet Survey of Younger US Consumers

QLIF-18. DIGITAL PHENOTYPING: A NOVEL METHOD FOR QUALITY OF LIFE ASSESSMENT IN PATIENTS WITH BRAIN TUMORS

Using a network-based approach and targeted maximum likelihood estimation to evaluate the effect of adding pre-exposure prophylaxis to an ongoing test-and-treat trial

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