The use of receiver operating characteristic curves in biomedical informatics TA Lasko, JG Bhagwat, KH Zou, L Ohno-Machado Journal of biomedical informatics 38 (5), 404-415, 2005 | 1048 | 2005 |
Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data TA Lasko, JC Denny, MA Levy PloS one 8 (6), e66341, 2013 | 381 | 2013 |
Calibration drift in regression and machine learning models for acute kidney injury SE Davis, TA Lasko, G Chen, ED Siew, ME Matheny Journal of the American Medical Informatics Association 24 (6), 1052-1061, 2017 | 248 | 2017 |
Portability of an algorithm to identify rheumatoid arthritis in electronic health records RJ Carroll, WK Thompson, AE Eyler, AM Mandelin, T Cai, RM Zink, ... Journal of the American Medical Informatics Association 19 (e1), e162-e169, 2012 | 242 | 2012 |
A study of active learning methods for named entity recognition in clinical text Y Chen, TA Lasko, Q Mei, JC Denny, H Xu Journal of biomedical informatics 58, 11-18, 2015 | 182 | 2015 |
Development and evaluation of an ensemble resource linking medications to their indications WQ Wei, RM Cronin, H Xu, TA Lasko, L Bastarache, JC Denny Journal of the American Medical Informatics Association 20 (5), 954-961, 2013 | 124 | 2013 |
Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals PL Teixeira, WQ Wei, RM Cronin, H Mo, JP VanHouten, RJ Carroll, ... Journal of the American Medical Informatics Association 24 (1), 162-171, 2016 | 99 | 2016 |
A nonparametric updating method to correct clinical prediction model drift SE Davis, RA Greevy Jr, C Fonnesbeck, TA Lasko, CG Walsh, ... Journal of the American Medical Informatics Association 26 (12), 1448-1457, 2019 | 94 | 2019 |
Melatonin suppression by illumination of upper and lower visual fields TA Lasko, DF Kripke, JA Elliot Journal of biological rhythms 14 (2), 122-125, 1999 | 94 | 1999 |
Predicting changes in hypertension control using electronic health records from a chronic disease management program J Sun, CD McNaughton, P Zhang, A Perer, A Gkoulalas-Divanis, ... Journal of the American Medical Informatics Association 21 (2), 337-344, 2013 | 83 | 2013 |
Detection of calibration drift in clinical prediction models to inform model updating SE Davis, RA Greevy Jr, TA Lasko, CG Walsh, ME Matheny Journal of Biomedical Informatics 112, 103611, 2020 | 65 | 2020 |
Predicting medications from diagnostic codes with recurrent neural networks JM Bajor, TA Lasko International conference on learning representations, 2016 | 64* | 2016 |
Is this “my” patient? Development and validation of a predictive model to link patients to primary care providers SJ Atlas, Y Chang, TA Lasko, HC Chueh, RW Grant, MJ Barry Journal of general internal medicine 21 (9), 973-978, 2006 | 63 | 2006 |
Efficient inference of Gaussian-process-modulated renewal processes with application to medical event data TA Lasko Uncertainty in artificial intelligence: proceedings of the... conference …, 2014 | 59 | 2014 |
Calibration Drift Among Regression and Machine Learning Models for Hospital Mortality SE Davis, TA Lasko, G Chen, ME Matheny AMIA Annual Symposium Proceedings 2017, 625, 2017 | 51 | 2017 |
Machine Learning for Risk Prediction of Acute Coronary Syndrome JP VanHouten, JM Starmer, NM Lorenzi, DJ Maron, TA Lasko AMIA Annual Symposium Proceedings 2014, 1940, 2014 | 51 | 2014 |
SynTEG: a framework for temporal structured electronic health data simulation Z Zhang, C Yan, TA Lasko, J Sun, BA Malin Journal of the American Medical Informatics Association 28 (3), 596-604, 2021 | 46 | 2021 |
Fully automatic liver attenuation estimation combing CNN segmentation and morphological operations Y Huo, JG Terry, J Wang, S Nair, TA Lasko, BI Freedman, JJ Carr, ... Medical physics 46 (8), 3508-3519, 2019 | 38 | 2019 |
Demystifying artificial intelligence in pharmacy SD Nelson, CG Walsh, CA Olsen, AJ McLaughlin, JR LeGrand, N Schutz, ... American Journal of Health-System Pharmacy 77 (19), 1556-1570, 2020 | 37 | 2020 |
Cost-aware active learning for named entity recognition in clinical text Q Wei, Y Chen, M Salimi, JC Denny, Q Mei, TA Lasko, Q Chen, S Wu, ... Journal of the American Medical Informatics Association 26 (11), 1314-1322, 2019 | 35 | 2019 |