| 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 | 141 | 2019 |
| Bounded extremum seeking with discontinuous dithers A Scheinker, D Scheinker Automatica 69, 250-257, 2016 | 93 | 2016 |
| 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 | 77 | 2020 |
| 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 | 69 | 2020 |
| 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 | 58 | 2022 |
| 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 | 55 | 2020 |
| 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 | 49 | 2017 |
| 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, 2019 | 48 | 2019 |
| 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 | 45 | 2019 |
| 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 | 36 | 2019 |
| 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 | 35 | 2018 |
| 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 | 34 | 2018 |
| 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 | 33 | 2021 |
| CGM metrics identify dysglycemic states in participants from the TrialNet pathway to prevention study DM Wilson, SL Pietropaolo, M Acevedo-Calado, S Huang, D Anyaiwe, ... Diabetes Care 46 (3), 526-534, 2023 | 32 | 2023 |
| 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 | 30 | 2020 |
| 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 | 25 | 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 | 25 | 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 | 24 | 2020 |
| 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 | 23 | 2021 |
| Prediction of prolonged opioid use after surgery in adolescents: insights from machine learning A Ward, T Jani, E De Souza, D Scheinker, N Bambos, TA Anderson Anesthesia & Analgesia 133 (2), 304-313, 2021 | 21 | 2021 |
David MaahsProfessor of Pediatric, Stanford UniversityVerified email at stanford.edu