Neighborhood-level risk factors for severe hyperglycemia among ED patients without a prior diabetes diagnosis
Christian Koziatek, Isaac Bohart, [...], Daniel B. Neill, David LeeRead more
PROVIDENT: development and validation of a machine learning model to predict neighborhood-level overdose risk in Rhode Island
Bennett Allen, [...], Magdalena Cerda, Daniel B. NeillRead more
Fairness and bias of machine learning in healthcare and medicine
Isaac Bohart, Daniel B. Neill, and David LeeRead more
Presyndromic surveillance for improved detection of emerging public health threats
Mallory Nobles, Ramona Lall, Robert W. Mathes, and Daniel B. NeillRead more
Preventing overdose using information and data from the environment (PROVIDENT): Protocol for a randomised, population-based, community intervention trial
B. D. L. Marshall, N. Alexander-Scott, J. L. Yedinak, B. Hallowell, W. C. Goedel, B. Allen, R. C. Schell, M. S. Krieger, C. Pratty, J. Ahern, D. B. Neill, and M. CerdaRead more
Provable detection of propagating sampling bias in prediction models
Pavan Ravishankar, Qingyu Mo, Edward McFowland III, and Daniel B. NeillRead more
Calibrated nonparametric scan statistics for anomalous pattern detection in graphs
Chunpai Wang, Daniel B. Neill, and Feng ChenRead more
Estimating reporting bias in 311 complaint data
Kate S. Boxer, Boyeoung Hong, Constantine E. Kontokosta, and Daniel B. NeillRead more
Identifying predictors of opioid overdose death at a neighborhood level with machine learning
R. C. Schell, B. Allen, W. C. Goedel, B. D. Hallowell, R. Scagos, Y. Li, M. S. Krieger, D. B. Neill, B. D. L. Marshall, M. Cerda, and J. AhernRead more
Detecting anomalous networks of opioid prescribers and dispensers in prescription drug data