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

Safranek, C. W., & Scheinker, D. (2022). A computer modeling method to analyze rideshare data for the surveillance of novel strains of SARS-CoV-2. Annals of epidemiology.

Zhang, T., McFarlane, K., Vallon, J., Yang, L., Xie, J., Blanchet, J., … & Scheinker, D. (2020). A Model of Bed Demand to Facilitate the Implementation of Data-driven Recommendations for COVID-19 Capacity Management.

Kong, S. T., Lee, R. Y., Rodriguez, F., & Scheinker, D. (2021). Racial and Ethnic Disparities in Household Contact with Individuals at Higher Risk of Exposure to COVID-19. Journal of General Internal Medicine, 1-3.

Yang, L., Zhang, T., Glynn, P., & Scheinker, D. (2021). The development and deployment of a model for hospital-level COVID-19 associated patient demand intervals from consistent estimators (DICE). Health Care Management Science, 1-27.

Ferstad, J. O., Gu, A. J., Lee, R. Y., Thapa, I., Shin, A. Y., Salomon, J. A., … & Scheinker, D. (2020). A model to forecast regional demand for COVID-19 related hospital beds. medRxiv.

Zhang, T., McFarlane, K., Vallon, J., Yang, L., Xie, J., Blanchet, J., … & Scheinker, D. (2020). A model to estimate bed demand for COVID-19 related hospitalization. medRxiv.

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 & Telemedicine

Dhillon, G. S., Miller, D. R., Bambos, N., Shin, A., & Scheinker, D. (2022). WAVES-The Lucile Packard Children’s Hospital Open-Access Pediatric Physiological Waveforms Dataset. Circulation, 146(Suppl_1), A14205-A14205.

Kalwani, N. M., Koos, H., Oesterle, A., Kohn, E. V., Parameswaran, V., Qureshi, L., … & Rodriguez, F. (2022). Impact of Telemedicine New Patient Visits on Total Visit Utilization at an Academic Cardiovascular Center: An Instrumental Variable Analysis. Circulation, 146(Suppl_1), A12277-A12277.

Koos, Harrison, Vijaya Parameswaran, Sahej Claire, Chelsea Chen, Neil Kalwani, Esli Osmanlliu, Lubna Qureshi, Rajesh Dash, David Scheinker, and Fatima Rodriguez. “Drivers of variation in telemedicine use during the COVID-19 pandemic: The experience of a large academic cardiovascular practice.” Journal of Telemedicine and Telecare (2022): 1357633X221130288.

Brandeau, Margaret L., and David Scheinker. “Analytics-Driven Capacity Management.” Artificial Intelligence for Healthcare: Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World (2022): 159.

Suen, Sze-chuan, David Scheinker, and Eva Enns, eds. Artificial Intelligence for Healthcare: Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World. Cambridge University Press, 2022.

Scheinker, David, Robert A. Harrington, and Fatima Rodriguez. “Practical Advice for Clinician–Engineer Partnerships for the Use of AI, Optimization, and Analytics for Healthcare Delivery.” Artificial Intelligence for Healthcare: Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World (2022): 182.

Scheinker, D., & Brandeau, M. L. (2020). Implementing Analytics Projects in a Hospital: Successes, Failures, and Opportunities. INFORMS Journal on Applied Analytics, 50(3), 176-189.

Muffly, M. K., Honkanen, A., Scheinker, D., Wang, T. N. Y., Saynina, O., Singleton, M. A., … & Sanders, L. (2020). Hospitalization patterns for inpatient pediatric surgery and procedures in California: 2000–2016. Anesthesia & Analgesia, 131(4), 1070-1079.

Shin, A. Y., Rao, I. J., Bassett, H. K., Chadwick, W., Kim, J., Kipps, A. K., … & Algaze, C. A. (2021). Target-Based Care: An Intervention to Reduce Variation in Postoperative Length of Stay. The Journal of Pediatrics, 228, 208-212.

Scheinker, D., Hollingsworth, M., Brody, A., Phelps, C., Bryant, W., Pei, F., … & Wall, J. (2021). The design and evaluation of a novel algorithm for automated preference card optimization. Journal of the American Medical Informatics Association.

Gal, D. B., Han, B., Longhurst, C., Scheinker, D., & Shin, A. Y. (2021). Quantifying Electronic Health Record Data: A Potential Risk for Cognitive Overload. Hospital Pediatrics, 11(2), 175-178.

Donnelly, L. F., Scheinker, D., Pageler, N. M., & Shin, A. Y. (2021). Correlation between an Independent Electronic Health Record & External Ranking of Children’s Hospitals. Health, 13(02), 81.

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, TIDE, and Continuous Glucose Monitors

Senanayake, R., Ferstad, J. O., Thapa, I., Giammarino, F., Vasu, M., Zaharieva, D., … & Scheinker, D. (2022). A Platform for the Personalized Management of Diabetes and Cardiovascular Disease at Population Scale With Data From Multiple Sensors. Circulation, 146(Suppl_1), A13358-A13358.

Scheinker, D., Gu, A., Grossman, J., Ward, A., Ayerdi, O., Miller, D., … & Prahalad, P. (2022). Algorithm-Enabled, Personalized Glucose Management for Type 1 Diabetes at the Population Scale: Prospective Evaluation in Clinical Practice. JMIR diabetes, 7(2), e27284.

PRAHALAD, P., DING, V., ZAHARIEVA, D. P., ADDALA, A., BISHOP, F. K., SCHEINKER, D., … & 4T STUDY GROUP. (2022). 40-OR: 4T Study: Tighter Glucose Target Settings with Early CGM Initiation Further Improves A1C in Youth with T1D. Diabetes, 71(Supplement_1).

PEI, R. L., DUPENLOUP, P., PRAHALAD, P., JOHARI, R., ADDALA, A., ZAHARIEVA, D. P., … & SCHEINKER, D. (2022). 888-P: A Model to Assess the Financial Feasibility of Telemedicine-Based Pediatric T1D Care in Value-Based Care and Fee-for-Service Settings. Diabetes, 71(Supplement_1).

Addala, A., V. Ding, F. Bishop, D. Zaharieva, A. Adams, A. King, R. Johari et al. “REDUCING DISPARITIES IN HEMOGLOBIN A1C DURING THE FIRST YEAR OF DIABETES DIAGNOSIS: ACCOMPLISHMENTS AND AREAS FOR IMPROVEMENT IN THE 4T STUDY.” In DIABETES TECHNOLOGY & THERAPEUTICS, vol. 24, pp. A60-A61. 140 HUGUENOT STREET, 3RD FL, NEW ROCHELLE, NY 10801 USA: MARY ANN LIEBERT, INC, 2022.

Prahalad, Priya, Victoria Y. Ding, Dessi P. Zaharieva, Ananta Addala, Ramesh Johari, David Scheinker, Manisha Desai, Korey Hood, and David M. Maahs. “Teamwork, Targets, Technology, and Tight Control in Newly Diagnosed Type 1 Diabetes: the Pilot 4T Study.” The Journal of Clinical Endocrinology & Metabolism 107, no. 4 (2022): 998-1008.

Zaharieva, D., Prahalad, P., Addala, A., Scheinker, D., Desai, M., Hood, K. K., … & Maahs, D. M. (2020). 1295-P: Newly Diagnosed Pediatric Patients with Type 1 Diabetes Show Steady Decline in Glucose Time-in-Range (TIR) over 1 Year: Pilot Study.

Vallon, J., Ward, A. T., Prahalad, P., Hood, K. K., Maahs, D. M., & Scheinker, D. (2020). 1185-P: A Telemedicine-CGM Recommendation System for Personalized Population Health Management.

Lee, M. Y., Leverenz, J., Leverenz, B., Pageler, N. M., Scheinker, D., Maahs, D. M., & Prahalad, P. (2020). 1281-P: Pilot Study on the Feasibility of Weekly CGM Data Review in Youth with New-Onset Type 1 Diabetes.

CHANG, A., GAO, M. Z., FERSTAD, J., MAAHS, D. M., PRAHALAD, P., JOHARI, R., & SCHEINKER, D. (2022). 1033-P: An Interactive Capacity Planning Dashboard for Algorithm-Enabled Telemedicine-Based Diabetes Care. Diabetes, 71(Supplement_1).

PEI, R. L., DUPENLOUP, P., PRAHALAD, P., JOHARI, R., ADDALA, A., ZAHARIEVA, D. P., … & SCHEINKER, D. (2022). 888-P: A Model to Assess the Financial Feasibility of Telemedicine-Based Pediatric T1D Care in Value-Based Care and Fee-for-Service Settings. Diabetes, 71(Supplement_1).

Grossman, J., Ward, A. T., Maahs, D. M., Prahalad, P., & Scheinker, D. (2020). 872-P: Improved HbA1c Estimation Using CGM Data.

FERSTAD, J., PRAHALAD, P., MAAHS, D. M., FOX, E., JOHARI, R., & SCHEINKER, D. (2022). 1009-P: The Association between Patient Characteristics and the Efficacy of Remote Patient Monitoring and Messaging. Diabetes, 71(Supplement_1).

Joshua Grossman, Andrew Ward, Jamie L. Crandell, Priya Prahalad, David M. Maahs, David Scheinker, Improved individual and population-level HbA1c estimation using CGM data and patient characteristics,  Journal of Diabetes and its Complications, Volume 35, Issue 8, 2021, 107950, ISSN 1056-8727.

Prahalad, P., Ding, V., Addala, A., New, C., Conrad, B. P., Chmielewski, A., … & Maahs, D. M. (2020). 1297-P: Early CGM Initiation Improves HbA1c in T1D Youth over the First 15 Months.

Gu, A., Prahalad, P., Maahs, D. M., Addala, A., & Scheinker, D. (2020). 1289-P: The Association between Time-in-Range, Mean Glucose, and Incidence of Hypoglycemia in Youth with Newly Diagnosed T1D.

Ananta Addala, Dessi P Zaharieva, Angela J Gu, Priya Prahalad, David Scheinker, Bruce Buckingham, Korey K Hood, David M Maahs, Clinically Serious Hypoglycemia Is Rare and Not Associated With Time-in-range in Youth With New-onset Type 1 Diabetes, The Journal of Clinical Endocrinology & Metabolism, Volume 106, Issue 11, November 2021, Pages 3239–3247

Addala, A., Gu, A., Zaharieva, D., Prahalad, P., Buckingham, B. A., Scheinker, D., & Maahs, D. M. (2020). 1294-P: Clinically Significant Hypoglycemia Is Rare in Youth with T1D during Partial Clinical Remission.

Addala, A., Maahs, D. M., Scheinker, D., Chertow, S., Leverenz, B., & Prahalad, P. (2020). Uninterrupted continuous glucose monitoring access is associated with a decrease in HbA1c in youth with type 1 diabetes and public insurance. Pediatric Diabetes, 21(7), 1301-1309.

Ferstad, JO, Vallon, JJ, Jun, D, et al. Population-level management of type 1 diabetes via continuous glucose monitoring and algorithm-enabled patient prioritization: Precision health meets population health. Pediatr Diabetes. 2021; 22( 7): 982– 991.

Knight, G. M., Spencer-Bonilla, G., Maahs, D. M., Blum, M. R., Valencia, A., Zuma, B. Z., … & Scheinker, D. (2020). Multimethod, multidataset analysis reveals paradoxical relationships between sociodemographic factors, Hispanic ethnicity and diabetes. BMJ Open Diabetes Research and Care, 8(2), e001725.

Prahalad, P., Zaharieva, D. P., Addala, A., New, C., Scheinker, D., Desai, M., … & Maahs, D. M. (2020). Improving Clinical Outcomes in Newly Diagnosed Pediatric Type 1 Diabetes: Teamwork, Targets, Technology, and Tight Control—The 4T Study. Frontiers in Endocrinology, 11.

PRAHALAD, P., DING, V., ZAHARIEVA, D. P., ADDALA, A., BISHOP, F. K., SCHEINKER, D., … & 4T STUDY GROUP. (2022). 40-OR: 4T Study: Tighter Glucose Target Settings with Early CGM Initiation Further Improves A1C in Youth with T1D. Diabetes, 71(Supplement_1).

Scheinker, D., Prahalad, P., Johari, R., Maahs, D. M., & Majzun, R. (2022). A New Technology-Enabled Care Model for Pediatric Type 1 Diabetes. NEJM Catalyst Innovations in Care Delivery, 3(5), CAT-21.

David Scheinker, PhD; Angela Gu; Josh Grossman, BA; Andrew Ward, PhD; Oseas Ayerdi, MS; Daniel Miller, PhD; Jeannine Leverenz, RN, CDE; Korey Hood, PhD; Ming Yeh Lee, MD, PhD; David M Maahs, MD, PhD; Priya Prahalad, MD, PhD. Algorithm-Enabled, Personalized Glucose Management for Type 1 Diabetes at the Population Scale: Prospective Evaluation in Clinical Practice

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).

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

Gonzalez, A. B., Mulet, Y. M., Song, N., Loh, L., Scheinker, D., Shin, A. Y., & Donnelly, L. F. (2022). Predictive Ability of the Braden QD Scale for Hospital-Acquired Venous Thromboembolism in Hospitalized Children. The Joint Commission Journal on Quality and Patient Safety, 48(10), 513-520.

Podboy, A. J., & Scheinker, D. (2020). Tu1970 MACHINE LEARNING CAN IMPROVE ESTIMATION OF COLONOSCOPY PROCEDURE DURATION: A PILOT STUDY. Gastroenterology, 158(6), S-1237.

Podboy, A. J., & Scheinker, D. (2020). Machine learning better predicts colonoscopy duration. Artificial Intelligence in Gastroenterology, 1(1), 30-36.

Ward, A., Sarraju, A., Chung, S., Li, J., Harrington, R., Heidenreich, P., … & Rodriguez, F. (2020). Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population. NPJ digital medicine, 3(1), 1-7.

Sarraju, A., Ward, A., Chung, S., Li, J., Scheinker, D., & Rodriguez, F. (2020). Prediction of Recurrent Atherosclerotic Cardiovascular Disease Risk Using Machine Learning and Electronic Health Record Data. Circulation, 142(Suppl_3), A17106-A17106.

Ward, A. T., Li, J., Sarraju, A., Valencia, A., Scheinker, D., & Rodriguez, F. (2020). Personalizing Cholesterol Management Therapy Using Electronic Medical Records and Machine Learning. Circulation, 142(Suppl_3), A17323-A17323.

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

Sarraju, A., Ngo, S., Ashland, M., Scheinker, D., & Rodriguez, F. (2022). Trends in national and county-level Hispanic mortality in the United States, 2011–2020. Scientific Reports, 12(1), 1-5.

Valencia, A., Zuma, B. Z., Spencer‐Bonilla, G., López, L., Scheinker, D., & Rodriguez, F. (2021). The Hispanic paradox in the prevalence of obesity at the county‐level. Obesity Science & Practice, 7(1), 14-24.

Scheinker, D., Richman, B. D., Milstein, A., & Schulman, K. A. (2021). Reducing administrative costs in US health care: Assessing single payer and its alternatives. Health services research.

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

Russell, W. A., Scheinker, D., & Sutherland, S. M. (2020). Baseline creatinine determination method impacts association between acute kidney injury and clinical outcomes. Pediatric Nephrology, 1-9.

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

Duong, S. Q., Shi, Y., Giacone, H., Navarre, B., Gal, D., Han, B., … & Algaze, C. A. (2022). Criteria for Early Pacemaker Implantation in Patients With Postoperative Heart Block After Congenital Heart Surgery. Circulation: Arrhythmia and Electrophysiology, e011145.

Augustine Chemparathy*; Martin G. Seneviratne, MD†; Andrew Ward, PhD‡; Simran Mirchandani, MS; Ron Li, MD§; Roshni Mathew, MD; Matthew Wood, PhD; Andrew Y. Shin, MD; Lane F. Donnelly, MD; David Scheinker, PhD*; Grace M. Lee, MD, MPH Development and Implementation of a Real-time Bundle-adherence Dashboard for Central Line-associated Bloodstream Infections. Pediatric Quality and Safety. In Press

Ward, A., Chemparathy, A., Donnelly, F., Lee, G.M., Mathew, M., Scheinker, D., and Shin, A. The Association Between Central Line-Associated Bloodstream Infection and Central Line Access. In Submission

Zuma, B., Valencia, A., Spencer-Bonilla, G., Blum, M. R., Knight, G., Scheinker, D., & Rodriguez, F. (2020). COUNTY-LEVEL FACTORS ASSOCIATED WITH CARDIOVASCULAR MORTALITY DISAGGREGATED BY RACE/ETHNICITY. Journal of the American College of Cardiology, 75(11_Supplement_1), 1884-1884.

Zuma, B. Z., Parizo, J. T., Valencia, A., Spencer‐Bonilla, G., Blum, M. R., Scheinker, D., & Rodriguez, F. (2021). County‐Level Factors Associated With Cardiovascular Mortality by Race/Ethnicity. Journal of the American Heart Association, 10(6), e018835.

Gamino, G., Parizo, J. T., Scheinker, D., & Rodriguez, F. (2020). Racial and Ethnic Minority Groups Are Under-Represented and Under-Reported in Guideline-Informing Heart Failure Clinical Trials. Circulation, 142(Suppl_3), A17339-A17339.

Scheinker, D., Ward, A., Shin, A. Y., Lee, G. M., Mathew, R., & Donnelly, L. F. (2020). Differences in Central Line–Associated Bloodstream Infection Rates Based on the Criteria Used to Count Central Line Days. Jama, 323(2), 183-185.

Ward, A., Sarraju, A., Chung, S., Palaniappan, L., Scheinker, D., & Rodriguez, F. (2019). Prediction of Atherosclerotic Cardiovascular Disease Risk Using Machine Learning and Electronic Health Record Data. Circulation, 140(Suppl_1), A11985-A11985.

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., & Scheinker, D. (2020). Extremum seeking for optimal control problems with unknown time‐varying systems and unknown objective functions. International Journal of Adaptive Control and Signal Processing.

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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