David Scheinker
Cited by
Cited by
Improving the efficiency of the operating room environment with an optimization and machine learning model
M Fairley, D Scheinker, ML Brandeau
Health care management science 22, 756-767, 2019
Bounded extremum seeking with discontinuous dithers
A Scheinker, D Scheinker
Automatica 69, 250-257, 2016
Improving predictions of pediatric surgical durations with supervised learning
N Master, Z Zhou, D Miller, D Scheinker, N Bambos, P Glynn
International Journal of Data Science and Analytics 4, 35-52, 2017
Uninterrupted continuous glucose monitoring access is associated with a decrease in HbA1c in youth with type 1 diabetes and public insurance
A Addala, DM Maahs, D Scheinker, S Chertow, B Leverenz, P Prahalad
Pediatric diabetes 21 (7), 1301-1309, 2020
CGM initiation soon after type 1 diabetes diagnosis results in sustained CGM use and wear time
P Prahalad, A Addala, D Scheinker, KK Hood, DM Maahs
Diabetes Care 43 (1), e3-e4, 2020
Improving clinical outcomes in newly diagnosed pediatric type 1 diabetes: teamwork, targets, technology, and tight control—The 4T Study
P Prahalad, DP Zaharieva, A Addala, C New, D Scheinker, M Desai, ...
Frontiers in Endocrinology 11, 360, 2020
Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population
A Ward, A Sarraju, S Chung, J Li, R Harrington, P Heidenreich, ...
NPJ digital medicine 3 (1), 125, 2020
Identification of factors associated with variation in US county-level obesity prevalence rates using epidemiologic vs machine learning models
D Scheinker, A Valencia, F Rodriguez
JAMA Network open 2 (4), e192884-e192884, 2019
Constrained extremum seeking stabilization of systems not affine in control
A Scheinker, D Scheinker
International Journal of Robust and Nonlinear Control 28 (2), 568-581, 2018
Promise and perils of big data and artificial intelligence in clinical medicine and biomedical research
F Rodriguez, D Scheinker, RA Harrington
Circulation Research 123 (12), 1282-1284, 2018
A model to forecast regional demand for COVID-19 related hospital beds
JO Ferstad, A Gu, RY Lee, I Thapa, AY Shin, JA Salomon, P Glynn, ...
MedRxiv, 2020.03. 26.20044842, 2020
A model to estimate bed demand for COVID-19 related hospitalization
T Zhang, K McFarlane, J Vallon, L Yang, J Xie, J Blanchet, P Glynn, ...
medRxiv, 2020.03. 24.20042762, 2020
Hemoglobin A1c trajectory in pediatric patients with newly diagnosed type 1 diabetes
P Prahalad, J Yang, D Scheinker, M Desai, K Hood, DM Maahs
Diabetes Technology & Therapeutics 21 (8), 456-461, 2019
Teamwork, targets, technology, and tight control in newly diagnosed type 1 diabetes: the pilot 4T study
P Prahalad, VY Ding, DP Zaharieva, A Addala, R Johari, D Scheinker, ...
The Journal of Clinical Endocrinology & Metabolism 107 (4), 998-1008, 2022
Wasted Health Spending: Who’s Picking Up The Tab?
DP O’Neill, D Scheinker
Health Affairs Forefront, 2018
Extremum seeking for optimal control problems with unknown time‐varying systems and unknown objective functions
A Scheinker, D Scheinker
International Journal of Adaptive Control and Signal Processing 35 (7), 1143 …, 2021
Implementing analytics projects in a hospital: successes, failures, and opportunities
D Scheinker, ML Brandeau
INFORMS Journal on Applied Analytics 50 (3), 176-189, 2020
Detecting inaccurate predictions of pediatric surgical durations
Z Zhou, D Miller, N Master, D Scheinker, N Bambos, P Glynn
2016 IEEE International Conference on Data Science and Advanced Analytics …, 2016
Reducing administrative costs in US health care: Assessing single payer and its alternatives
D Scheinker, BD Richman, A Milstein, KA Schulman
Health Services Research 56 (4), 615-625, 2021
Population‐level management of type 1 diabetes via continuous glucose monitoring and algorithm‐enabled patient prioritization: Precision health meets population health
JO Ferstad, JJ Vallon, D Jun, A Gu, A Vitko, DP Morales, J Leverenz, ...
Pediatric Diabetes 22 (7), 982-991, 2021
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