| Deep representation learning of electronic health records to unlock patient stratification at scale I Landi, BS Glicksberg, HC Lee, S Cherng, G Landi, M Danieletto, ... NPJ digital medicine 3 (1), 96, 2020 | 193 | 2020 |
| Evaluation of a personalized, web-based decision aid for lung cancer screening YK Lau, TJ Caverly, P Cao, ST Cherng, M West, C Gaber, D Arenberg, ... American Journal of Preventive Medicine 49 (6), e125-e129, 2015 | 99 | 2015 |
| Modeling the effects of e-cigarettes on smoking behavior: implications for future adult smoking prevalence ST Cherng, J Tam, PJ Christine, R Meza Epidemiology 27 (6), 819-826, 2016 | 77 | 2016 |
| A participatory model of the paradox of primary care L Homa, J Rose, PS Hovmand, ST Cherng, RL Riolo, A Kraus, A Biswas, ... The Annals of Family Medicine 13 (5), 456-465, 2015 | 64 | 2015 |
| Scaling structural learning with NO-BEARS to infer causal transcriptome networks HC Lee, M Danieletto, R Miotto, ST Cherng, JT Dudley Pacific Symposium on Biocomputing 2020, 391-402, 2019 | 49 | 2019 |
| Development and validation of a personalized, web-based decision aid for lung cancer screening using mixed methods: a study protocol YK Lau, TJ Caverly, ST Cherng, P Cao, M West, D Arenberg, R Meza JMIR research protocols 3 (4), e4039, 2014 | 49 | 2014 |
| Online reactions to the 2017 ‘Unite the right’rally in Charlottesville: measuring polarization in Twitter networks using media followership JH Tien, MC Eisenberg, ST Cherng, MA Porter Applied Network Science 5, 1-27, 2020 | 42 | 2020 |
| Provider dismissal policies and clustering of vaccine-hesitant families: an agent-based modeling approach AM Buttenheim, ST Cherng, DA Asch Human vaccines & immunotherapeutics 9 (8), 1819-1824, 2013 | 37 | 2013 |
| From epidemiologic knowledge to improved health: a vision for translational epidemiology M Windle, HD Lee, ST Cherng, CR Lesko, C Hanrahan, JW Jackson, ... American journal of epidemiology 188 (12), 2049-2060, 2019 | 23 | 2019 |
| Social cohesion and passive adaptation in relation to climate change and disease ST Cherng, I Cangemi, JA Trostle, JV Remais, JNS Eisenberg Global Environmental Change 58, 101960, 2019 | 21 | 2019 |
| Impact and effectiveness of state-level tuberculosis interventions in California, Florida, New York, and Texas: a model-based analysis S Shrestha, S Cherng, AN Hill, S Reynolds, J Flood, PM Barry, ... American journal of epidemiology 188 (9), 1733-1741, 2019 | 21 | 2019 |
| Forecasting and uncertainty in modeling the 2014-2015 ebola epidemic in west africa MC Eisenberg, JNS Eisenberg, JP D'Silva, EV Wells, S Cherng, YH Kao, ... arXiv preprint arXiv:1501.05555, 2015 | 16 | 2015 |
| Complex systems Model of dynamic mechanisms of early childhood Caries Development B Heaton, ST Cherng, W Sohn, RI Garcia, S Galea Journal of dental research 99 (5), 537-543, 2020 | 15 | 2020 |
| Modeling the paradox of primary care J Rose, R Riolo, P Hovmand, S Cherng, R Ferrer, DA Katerndahl, ... Handbook of systems and complexity in health, 815-825, 2013 | 15 | 2013 |
| Boundary objects for participatory group model building of agent-based models J Rose, L Homa, P Hovmand, A Kraus, K Burgess, A Biswas, H Aungst, ... 2015 48th Hawaii international conference on system sciences, 2950-2959, 2015 | 12 | 2015 |
| A systematic approach towards missing lab data in electronic health records: A case study in non‐small cell lung cancer and multiple myeloma A Sondhi, J Weberpals, P Yerram, C Jiang, M Taylor, M Samant, ... CPT: Pharmacometrics & Systems Pharmacology 12 (9), 1201-1212, 2023 | 9 | 2023 |
| Deep representation learning of electronic health records to unlock patient stratification at scale. npj Digital Medicine, 3 (1): 96–96, 2020. 10.1038/s41746-020-0301-z I Landi, BS Glicksberg, HC Lee, S Cherng, G Landi, M Danieletto, ... July, 2020 | 8 | 2020 |
| Tuberculosis incidence among populations at high risk in California, Florida, New York, and Texas, 2011–2015 ST Cherng, S Shrestha, S Reynolds, AN Hill, SM Marks, J Kelly, ... American journal of public health 108 (S4), S311-S314, 2018 | 8 | 2018 |
| Enhancing high-content imaging for studying microtubule networks at large-scale HC Lee, ST Cherng, R Miotto, JT Dudley Machine Learning for Healthcare Conference, 592-613, 2019 | 7 | 2019 |
| Ai1 quantifying bias in ML-extracted variables for inference in clinical oncology J Lee, M Estevez, BD Segal, A Sondhi, AB Cohen, ST Cherng Value in Health 24, S1, 2021 | 1 | 2021 |
Rafael MezaBC Cancer Research InstituteVerified email at bccrc.ca
