Selected publications

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COVID-19

Hospital Operations

Diabetes and Continuous Glucose Monitors

Machine Learning and Artificial Intelligence in Health Care

Population Health, Policy, and Spending

Predictions, Risk, and Other Observational Clinical Studies

Heart Failure

Functional Analysis and Functions of Several Complex Variables

Laboratory Utilization

Extremum Seeking for Control

Find complete lists of publications for David Scheinker here and Andy Shin here.

 

COVID-19

Interactive Model for Hospitals to Estimate COVID-19-Related Bed and Ventilator Demand Developed by Stanford Medicine-Engineering Partnership

Stanford Medicine-Engineering Partnership Launches an Interactive Model to Facilitate COVID-19 Response Planning for Hospital and Regional Leaders

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Hospital Operations

Scheinker D, Brandeau ML. Implementing Analytics Projects in a Hospital: Successes, Failures, and Opportunities. INFORMS Journal on Applied Analytics. 2020 May 5.

Scheinker D, Ward A, Shin AY, Lee GM, Mathew R, Donnelly LF. Differences in Central Line–Associated Bloodstream Infection Rates Based on the Criteria Used to Count Central Line Days. JAMA. 2020;323(2):183–185. doi:10.1001/jama.2019.18616

Fairley, M., Scheinker, D. and Brandeau, M.L., 2018. Improving the efficiency of the operating room environment with an optimization and machine learning model. Health care management science, pp.1-12.

Caruso, T.J., Wang, E., Schwenk, H.T., Scheinker, D., Yeverino, C., Tweedy, M., Maheru, M. and Sharek, P.J., 2017. A quality improvement initiative to optimize dosing of surgical antimicrobial prophylaxis. Pediatric Anesthesia, 27(7), 702-710.

Sheth, S., McCarthy, E., Kipps, A.K., Wood, M., Roth, S.J., Sharek, P.J. and Shin, A.Y., 2016. Changes in efficiency and safety culture after integration of an I-PASS–supporte handoff process. Pediatrics, 137(2), p.e20150166.

Presentations and Posters

Scheinker, D. and Brandeau, M.L., 2017, June. Analytical Approaches to Operating Room Management. In International Conference on Health Care Systems Engineering (pp. 17-26). Springer, Cham.

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Diabetes and Continuous Glucose Monitors

Prahalad, P., Addala, A., Scheinker, D., Hood, K.K. and Maahs, D.M., 2019. CGM Initiation Soon After Type 1 Diabetes Diagnosis Results in Sustained CGM Use and Wear Time. Diabetes Care, p.dc191205.

Prahalad, Priya, Jaden Yang, David Scheinker, Manisha Desai, Korey Hood, and David M. Maahs. “Hemoglobin A1c Trajectory in Pediatric Patients with Newly Diagnosed Type 1 Diabetes.” Diabetes technology & therapeutics (2019).

Zheng, L., Wang, Y., Hao, S., Shin, A.Y., Jin, B., Ngo, A.D., Jackson-Browne, M.S., Feller, D.J., Fu, T., Zhang, K. and Zhou, X., 2016. Web-based real-time case finding for the population health Management of Patients with Diabetes Mellitus: a prospective validation of the natural language processing–based algorithm with statewide electronic medical records. JMIR medical informatics, 4(4).

Presentations and Posters

Miller, D.R., Ward, A.T., Maah, D.M. and Scheinker, D., 2019. 960-P: Personalized Diabetes Management Using Data from Continuous Glucose Monitors.

PRAHALAD, PRIYA, DAVID SCHEINKER, KOREY K. HOOD, BRUCE A. BUCKINGHAM, DARRELL M. WILSON, ANNETTE CHMIELEWSKI, BARRY P. CONRAD et al. “1358-P: Early CGM Initiation in New-Onset Type 1 Diabetes Patients.” (2019): 1358-P.

WILSON, DARRELL M., PATRICK NELSON, DAVID SCHEINKER, SUSAN PIETROPAOLO, M. A. R. I. A. ACEVEDO-CALADO, MAHDI EBRAHIMI, ANDREA STECK, JESSICA L. DUNNE, CARLA GREENBAUM, and MASSIMO PIETROPAOLO. “898-P: CGM Metrics Identify Dysglycemic States in Subjects with Normal OGTT from the TrialNet Pathway to Prevention Study.” (2019): 898-P.

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Machine Learning and Artificial Intelligence in Health Care

Rodriguez, F., Scheinker, D. and Harrington, R.A., 2018. Promise and Perils of Big Data and Artificial Intelligence in Clinical Medicine and Biomedical Research. Circulation research, 123(12), pp.1282-1284.

Master, N., Zhou, Z., Miller, D., Scheinker, D., Bambos, N. and Glynn, P., 2017. Improving predictions of pediatric surgical durations with supervised learning. International Journal of Data Science and Analytics,4(1), 33-52.

Master, Neal, David Scheinker, and Nicholas Bambos. “Predicting pediatric surgical durations.” arXiv preprint arXiv:1605.04574 (2016).

Presentations and Posters

Ward, Andrew, Ashish Sarraju, Sukyung Chung, Latha Palaniappan, David Scheinker, and Fatima Rodriguez. “Prediction of Atherosclerotic Cardiovascular Disease Risk Using Machine Learning and Electronic Health Record Data.” Circulation 140, no. Suppl_1 (2019): A11985-A11985.

Ward, Andrew, Zhengyuan Zhou, Nicholas Bambos, Ellen Wang, and David Scheinker. “Anesthesiologist Surgery Assignments using Policy Learning.” In ICC 2019-2019 IEEE International Conference on Communications (ICC), pp. 1-6. IEEE, 2019.

Miller, D., Ward, A., Bambos, N., Scheinker, D. and Shin, A., 2018, September. Physiological Waveform Imputation of Missing Data using Convolutional Autoencoders. In 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) (pp. 1-6). IEEE.

Miller, D., Scheinker, D. and Bambos, N., 2017, June. A Practical Approach to Machine Learning for Clinical Decision Support. In International Conference on Health Care Systems Engineering (pp. 111-120). Springer, Cham.

Zhou, Z., Miller, D., Master, N., Scheinker, D., Bambos, N. and Glynn, P., 2016. Detecting inaccurate predictions of pediatric surgical durations. Proceedings of 2016 IEEE International Conference on Data Science and Advanced Analytics, 452-45.

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Population Health, Policy, and Spending

Muffly, Matthew, David Scheinker, Tyler Muffly, Mark Singleton, Rita Agarwal, and Anita Honkanen. “Practice Characteristics of Board-certified Pediatric Anesthesiologists in the US: A Nationwide Survey.” (2019).

Scheinker, D., Valencia, A. and Rodriguez, F., 2019. Identification of Factors Associated With Variation in US County-Level Obesity Prevalence Rates Using Epidemiologic vs Machine Learning Models. JAMA network open, 2(4), pp.e192884-e192884.

Valencia, Areli, Bongeka Z. Zuma, Gabriel M. Knight, David Scheinker, Ashish Sarraju, and Fatima Rodriguez. “The Hispanic Paradox in County-level Obesity Prevalence.” Circulation 140, no. Suppl_1 (2019): A11918-A11918.

O’Neill, Daniel P., and David Scheinker. “Wasted Health Spending: Who’s Picking Up The Tab?.” Health affairs blog (2018).

Presentations and Posters

Valencia, Areli, Fatima Rodriguez, and David Scheinker. “EXPLAINING VARIATION IN US COUNTY-LEVEL OBESITY PREVALENCE.” Journal of the American College of Cardiology 73, no. 9 Supplement 1 (2019): 1762.

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Predictions, Risk, and Other Observational Clinical Studies

Muffly, Matthew, David Scheinker, Tyler Muffly, Mark Singleton, Rita Agarwal, and Anita Honkanen. “Practice Characteristics of Board-certified Pediatric Anesthesiologists in the US: A Nationwide Survey.” (2019).

Ward, Andrew, Ashish Sarraju, Sukyung Chung, Latha Palaniappan, David Scheinker, and Fatima Rodriguez. “Prediction of Atherosclerotic Cardiovascular Disease Risk Using Machine Learning and Electronic Health Record Data.” Circulation 140, no. Suppl_1 (2019): A11985-A11985.

Caruso, T.J., Tsui, J.H., Wang, E., Scheinker, D., Sharek, P.J., Cunningham, C. and Rodriguez, S.T., 2018. A Retrospective Review of a Bed-mounted Projection System for Managing Pediatric Preoperative Anxiety. Pediatric Quality & Safety, 3(4), p.e087.

Muffly, M.K., Singleton, M., Agarwal, R., Scheinker, D., Miller, D., Muffly, T.M. and Honkanen, A., 2018. The Pediatric Anesthesiology Workforce: Projecting Supply and Trends 2015-2035. Anesthesia & Analgesia, 126(2), pp.568-578.

Jin, B., Zhao, Y., Hao, S., Shin, A.Y., Wang, Y., Zhu, C., Hu, Z., Fu, C., Ji, J., Wang, Y. and Zhao, Y., 2016. Prospective stratification of patients at risk for emergency department revisit: resource utilization and population management strategy implications. BMC emergency medicine, 16(1), p.10.

Hao, S., Wang, Y., Jin, B., Shin, A.Y., Zhu, C., Huang, M., Zheng, L., Luo, J., Hu, Z., Fu, C. and Dai, D., 2015. Development, validation and deployment of a real time 30 day hospital readmission risk assessment tool in the Maine Healthcare Information Exchange. PloS one, 10(10), p.e0140271.

Hu, Z., Hao, S., Jin, B., Shin, A.Y., Zhu, C., Huang, M., Wang, Y., Zheng, L., Dai, D., Culver, D.S. and Alfreds, S.T., 2015. Online prediction of health care utilization in the next six months based on electronic health record information: a cohort and validation study. Journal of medical Internet research, 17(9).

Hu, Z., Jin, B., Shin, A.Y., Zhu, C., Zhao, Y., Hao, S., Zheng, L., Fu, C., Wen, Q., Ji, J. and Li, Z., 2015. Real-time web-based assessment of total population risk of future emergency department utilization: statewide prospective active case finding study. Interactive journal of medical research, 4(1).

Hao, S., Jin, B.O., Shin, A.Y., Zhao, Y., Zhu, C., Li, Z., Hu, Z., Fu, C., Ji, J., Wang, Y. and Zhao, Y., 2014. Risk prediction of emergency department revisit 30 days post discharge: a prospective study. PloS one, 9(11), p.e112944.

Presentations and Posters

Bentley, Jason P., Mariam Askari, Jun Fan, Paul A. Heidenreich, Kenneth W. Mahaffey, Manisha Desai, David Scheinker, and Mintu P. Turakhia. “Estimation of Stroke Outcomes in Atrial Fibrillation Using Continuous Clinical and Implantable Device Data From the Treat-AF Study: A Comparison With CHA2DS2-VASc Score.” Circulation 138, no. Suppl_1 (2018): A15123-A15123.

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Heart Failure

Maher, K.O., Chang, A.C., Shin, A., Hunt, J. and Wong, H.R., 2015. Innovation in pediatric cardiac intensive care: an exponential convergence toward transformation of care. World Journal for Pediatric and Congenital Heart Surgery, 6(4), pp.588-596.

Shin, A.Y., Jin, B., Hao, S., Hu, Z., Sutherland, S., McCammond, A., Axelrod, D., Sharek, P., Roth, S.J. and Ling, X.B., 2015. Utility of clinical biomarkers to predict central line-associated bloodstream infections after congenital heart surgery. The Pediatric infectious disease journal, 34(3), pp.251-254.

Wang, Y., Luo, J., Hao, S., Xu, H., Shin, A.Y., Jin, B., Liu, R., Deng, X., Wang, L., Zheng, L. and Zhao, Y., 2015. NLP based congestive heart failure case finding: A prospective analysis on statewide electronic medical records. International journal of medical informatics, 84(12), pp.1039-1047.

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Functional Analysis and Functions of Several Complex Variables

Scheinker, D., 2014. Hilbert function spaces and the Nevanlinna–Pick problem on the polydisc II. Journal of Functional Analysis, 266(1), pp.355-367.

Scheinker, D., 2013. A uniqueness theorem for bounded analytic functions on the polydisc. Complex Analysis and Operator Theory, 7(5), pp.1429-1436.

Scheinker, David. “Bounded analytic functions on the polydisc.” PhD diss., UC San Diego, 2011.

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Laboratory Utilization

Algaze, C.A., Wood, M., Pageler, N.M., Sharek, P.J., Longhurst, C.A. and Shin, A.Y., 2016. Use of a checklist and clinical decision support tool reduces laboratory use and improves cost. Pediatrics, 137(1), p.e20143019.

Pageler, N.M., Franzon, D., Longhurst, C.A., Wood, M., Shin, A.Y., Adams, E.S., Widen, E. and Cornfield, D.N., 2013. Embedding time-limited laboratory orders within computerized provider order entry reduces laboratory utilization. Pediatric Critical Care Medicine, 14(4), pp.413-419.

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Extremum Seeking for Control

Scheinker, A. and Scheinker, D., 2018. Constrained extremum seeking stabilization of systems not affine in control. International Journal of Robust and Nonlinear Control, 28(2), pp.568-581.

Scheinker, Alexander, and David Scheinker. “Extremum Seeking Optimal Controls of Unknown Systems.” arXiv preprint arXiv:1808.05181 (2018).

Scheinker, A. and Scheinker, D., 2016. Bounded extremum seeking with discontinuous dithers. Automatica, 69, pp.250-257.

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