Ian Barnett
Ian Barnett
Assistant Professor of Biostatistics, University of Pennsylvania
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Zitiert von
Zitiert von
Relapse prediction in schizophrenia through digital phenotyping: a pilot study
I Barnett, J Torous, P Staples, L Sandoval, M Keshavan, JP Onnela
Neuropsychopharmacology 43 (8), 1660-1666, 2018
Smartphones, sensors, and machine learning to advance real-time prediction and interventions for suicide prevention: a review of current progress and next steps
J Torous, ME Larsen, C Depp, TD Cosco, I Barnett, MK Nock, J Firth
Current psychiatry reports 20, 1-6, 2018
Detecting rare variant effects using extreme phenotype sampling in sequencing association studies
IJ Barnett, S Lee, X Lin
Genetic epidemiology 37 (2), 142-151, 2013
Change point detection in correlation networks
I Barnett, JP Onnela
Scientific reports 6 (1), 18893, 2016
The generalized higher criticism for testing SNP-set effects in genetic association studies
I Barnett, R Mukherjee, X Lin
Journal of the American Statistical Association 112 (517), 64-76, 2017
Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia
J Torous, P Staples, I Barnett, LR Sandoval, M Keshavan, JP Onnela
NPJ digital medicine 1 (1), 15, 2018
Inferring mobility measures from GPS traces with missing data
I Barnett, JP Onnela
Biostatistics 21 (2), e98-e112, 2020
A comparison of passive and active estimates of sleep in a cohort with schizophrenia
P Staples, J Torous, I Barnett, K Carlson, L Sandoval, M Keshavan, ...
NPJ schizophrenia 3 (1), 37, 2017
Ethics, transparency, and public health at the intersection of innovation and Facebook's suicide prevention efforts
I Barnett, J Torous
Annals of internal medicine 170 (8), 565-566, 2019
Heart rate variability and DNA methylation levels are altered after short-term metal fume exposure among occupational welders: a repeated-measures panel study
T Fan, SC Fang, JM Cavallari, IJ Barnett, Z Wang, L Su, HM Byun, X Lin, ...
BMC Public Health 14, 1-8, 2014
Beyond smartphones and sensors: choosing appropriate statistical methods for the analysis of longitudinal data
I Barnett, J Torous, P Staples, M Keshavan, JP Onnela
Journal of the American Medical Informatics Association 25 (12), 1669-1674, 2018
Digital phenotyping in patients with spine disease: a novel approach to quantifying mobility and quality of life
DJ Cote, I Barnett, JP Onnela, TR Smith
World neurosurgery 126, e241-e249, 2019
Digital phenotyping for psychiatry: Accommodating data and theory with network science methodologies
DM Lydon-Staley, I Barnett, TD Satterthwaite, DS Bassett
Current Opinion in Biomedical Engineering 9, 8-13, 2019
Ideas for how informaticians can get involved with COVID-19 research
JH Moore, I Barnett, MR Boland, Y Chen, G Demiris, ...
BioData mining 13, 1-16, 2020
Analytical p-value calculation for the higher criticism test in finite-d problems
IJ Barnett, X Lin
Biometrika 101 (4), 964-970, 2014
Towards clinically actionable digital phenotyping targets in schizophrenia
P Henson, I Barnett, M Keshavan, J Torous
npj Schizophrenia 6 (1), 13, 2020
Social and spatial clustering of people at humanity’s largest gathering
I Barnett, T Khanna, JP Onnela
PloS one 11 (6), e0156794, 2016
Neural networks for clustered and longitudinal data using mixed effects models
F Mandel, RP Ghosh, I Barnett
Biometrics 79 (2), 711-721, 2023
Smartphone relapse prediction in serious mental illness: a pathway towards personalized preventive care
J Torous, T Choudhury, I Barnett, M Keshavan, J Kane
World Psychiatry 19 (3), 308, 2020
Psychiatry outpatients’ willingness to share social media posts and smartphone data for research and clinical purposes: Survey study
A Rieger, A Gaines, I Barnett, CF Baldassano, MBC Gibbons, ...
JMIR formative research 3 (3), e14329, 2019
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