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 | 396 | 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 | 218 | 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 | 164 | 2013 |
Change point detection in correlation networks I Barnett, JP Onnela Scientific reports 6 (1), 18893, 2016 | 117 | 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 | 114 | 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 | 111 | 2018 |
Inferring mobility measures from GPS traces with missing data I Barnett, JP Onnela Biostatistics 21 (2), e98-e112, 2020 | 80 | 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 | 61 | 2017 |
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 | 53 | 2018 |
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 | 51 | 2019 |
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 | 49 | 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 | 49 | 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 | 46 | 2014 |
Towards clinically actionable digital phenotyping targets in schizophrenia P Henson, I Barnett, M Keshavan, J Torous npj Schizophrenia 6 (1), 13, 2020 | 41 | 2020 |
Neural networks for clustered and longitudinal data using mixed effects models F Mandel, RP Ghosh, I Barnett Biometrics 79 (2), 711-721, 2023 | 40 | 2023 |
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 | 35 | 2020 |
Analytical p-value calculation for the higher criticism test in finite-d problems IJ Barnett, X Lin Biometrika 101 (4), 964-970, 2014 | 33 | 2014 |
Determining sample size and length of follow-up for smartphone-based digital phenotyping studies I Barnett, J Torous, HT Reeder, J Baker, JP Onnela Journal of the American Medical Informatics Association 27 (12), 1844-1849, 2020 | 30 | 2020 |
Social and spatial clustering of people at humanity’s largest gathering I Barnett, T Khanna, JP Onnela PloS one 11 (6), e0156794, 2016 | 30 | 2016 |
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 | 26 | 2020 |