| 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 | 58 | 2020 |
| COVID-19 is associated with higher risk of venous thrombosis, but not arterial thrombosis, compared with influenza: Insights from a large US cohort A Ward, A Sarraju, D Lee, K Bhasin, S Gad, R Beetel, S Chang, ... PLoS One 17 (1), e0261786, 2022 | 28 | 2022 |
| 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 | 19 | 2021 |
| Incidence of and factors associated with prolonged and persistent postoperative opioid use in children 0–18 years of age A Ward, E De Souza, D Miller, E Wang, EC Sun, N Bambos, TA Anderson Anesthesia & Analgesia 131 (4), 1237-1248, 2020 | 19 | 2020 |
| Automatic sleep arousal identification from physiological waveforms using deep learning D Miller, A Ward, N Bambos 2018 Computing in Cardiology Conference (CinC) 45, 1-4, 2018 | 17 | 2018 |
| Machine learning approaches improve risk stratification for secondary cardiovascular disease prevention in multiethnic patients A Sarraju, A Ward, S Chung, J Li, D Scheinker, F Rodríguez Open Heart 8 (2), e001802, 2021 | 14 | 2021 |
| Physiological waveform imputation of missing data using convolutional autoencoders D Miller, A Ward, N Bambos, D Scheinker, A Shin 2018 IEEE 20th international conference on E-health networking, applications …, 2018 | 13 | 2018 |
| Algorithm-enabled, personalized glucose management for type 1 diabetes at the population scale: Prospective evaluation in clinical practice D Scheinker, A Gu, J Grossman, A Ward, O Ayerdi, D Miller, J Leverenz, ... JMIR diabetes 7 (2), e27284, 2022 | 12 | 2022 |
| Improved individual and population-level HbA1c estimation using CGM data and patient characteristics J Grossman, A Ward, JL Crandell, P Prahalad, DM Maahs, D Scheinker Journal of Diabetes and its Complications 35 (8), 107950, 2021 | 9 | 2021 |
| Learning health state transition probabilities via wireless body area networks T Geller, YB David, E Khmelnitsky, I Ben-Gal, A Ward, D Miller, N Bambos ICC 2019-2019 IEEE International Conference on Communications (ICC), 1-6, 2019 | 9 | 2019 |
| Personalizing cholesterol treatment recommendations for primary cardiovascular disease prevention A Sarraju, A Ward, J Li, A Valencia, L Palaniappan, D Scheinker, ... Scientific Reports 12 (1), 23, 2022 | 7 | 2022 |
| Smart greedy distributed allocation in microgrids I Bistritz, A Ward, Z Zhou, N Bambos ICC 2019-2019 IEEE International Conference on Communications (ICC), 1-6, 2019 | 7 | 2019 |
| Optimal health monitoring via wireless body area networks YB David, T Geller, E Khmelnitsky, I Ben-Gal, A Ward, D Miller, N Bambos 2018 IEEE Conference on Decision and Control (CDC), 6800-6805, 2018 | 7 | 2018 |
| Development and implementation of a real-time bundle-adherence dashboard for central line-associated bloodstream infections A Chemparathy, MG Seneviratne, A Ward, S Mirchandani, R Li, R Mathew, ... Pediatric Quality & Safety 6 (4), e431, 2021 | 6 | 2021 |
| Differences in central line–associated bloodstream infection rates based on the criteria used to count central line days D Scheinker, A Ward, AY Shin, GM Lee, R Mathew, LF Donnelly JAMA 323 (2), 183-185, 2020 | 6 | 2020 |
| Noninvasive identification of hypotension using convolutional-deconvolutional networks D Miller, A Ward, N Bambos, A Shin, D Scheinker 2019 IEEE International Conference on E-health Networking, Application …, 2019 | 4 | 2019 |
| Anesthesiologist surgery assignments using policy learning A Ward, Z Zhou, N Bambos, E Wang, D Scheinker ICC 2019-2019 IEEE International Conference on Communications (ICC), 1-6, 2019 | 4 | 2019 |
| Operationally-informed hospital-wide discharge prediction using machine learning A Ward, A Mann, J Vallon, G Escobar, N Bambos, A Schuler 2020 IEEE International Conference on E-Health Networking, Application …, 2021 | 3 | 2021 |
| Learning to emulate an expert projective cone scheduler A Ward, N Master, N Bambos 2019 American Control Conference (ACC), 292-297, 2019 | 3 | 2019 |
| Sociodemographic determinants of oral anticoagulant prescription in patients with atrial fibrillations: findings from the PINNACLE registry using machine learning Z Azizi, AT Ward, DJ Lee, SS Gad, K Bhasin, RJ Beetel, T Ferreira, ... Heart rhythm O2 4 (3), 158-168, 2023 | 2 | 2023 |
David ScheinkerStanford UniversityVerified email at stanford.edu
