| Use of machine learning models to predict death after acute myocardial infarction R Khera, J Haimovich, NC Hurley, R McNamara, JA Spertus, N Desai, ... JAMA cardiology 6 (6), 633-641, 2021 | 186 | 2021 |
| Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: a retrospective cohort study C Huang, K Murugiah, S Mahajan, SX Li, SS Dhruva, JS Haimovich, ... PLoS medicine 15 (11), e1002703, 2018 | 123 | 2018 |
| Continuous time level crossing sampling ADC for bio-potential recording systems W Tang, A Osman, D Kim, B Goldstein, C Huang, B Martini, VA Pieribone, ... IEEE Transactions on Circuits and Systems I: Regular Papers 60 (6), 1407-1418, 2013 | 105 | 2013 |
| Comparison of machine learning methods with national cardiovascular data registry models for prediction of risk of bleeding after percutaneous coronary intervention BJ Mortazavi, EM Bucholz, NR Desai, C Huang, JP Curtis, FA Masoudi, ... JAMA network open 2 (7), e196835-e196835, 2019 | 77 | 2019 |
| Prevalence, awareness, and treatment of isolated diastolic hypertension: insights from the China PEACE million persons project S Mahajan, D Zhang, S He, Y Lu, A Gupta, ES Spatz, J Lu, C Huang, ... Journal of the American Heart Association 8 (19), e012954, 2019 | 54 | 2019 |
| Performance metrics for the comparative analysis of clinical risk prediction models employing machine learning C Huang, SX Li, C Caraballo, FA Masoudi, JS Rumsfeld, JA Spertus, ... Circulation: Cardiovascular Quality and Outcomes 14 (10), e007526, 2021 | 49 | 2021 |
| Development and validation of a model for predicting the risk of acute kidney injury associated with contrast volume levels during percutaneous coronary intervention C Huang, SX Li, S Mahajan, JM Testani, FP Wilson, CI Mena, FA Masoudi, ... JAMA network open 2 (11), e1916021-e1916021, 2019 | 38 | 2019 |
| 4-channel asynchronous bio-potential recording system W Tang, C Huang, D Kim, B Martini, E Culurciello 2010 IEEE International Symposium on Circuits and Systems (ISCAS), 953-956, 2010 | 26 | 2010 |
| Toward dynamic risk prediction of outcomes after coronary artery bypass graft: improving risk prediction with intraoperative events using gradient boosting M Mori, TJS Durant, C Huang, BJ Mortazavi, A Coppi, RA Jean, ... Circulation: Cardiovascular Quality and Outcomes 14 (6), e007363, 2021 | 24 | 2021 |
| Leveraging the electronic health records for population health: a case study of patients with markedly elevated blood pressure Y Lu, C Huang, S Mahajan, WL Schulz, K Nasir, ES Spatz, HM Krumholz Journal of the American Heart Association 9 (7), e015033, 2020 | 22 | 2020 |
| Systolic blood pressure response in SPRINT (Systolic Blood Pressure Intervention Trial) and ACCORD (Action to Control Cardiovascular Risk in Diabetes): a possible explanation … C Huang, SS Dhruva, AC Coppi, F Warner, SX Li, H Lin, K Nasir, ... Journal of the American Heart Association 6 (11), e007509, 2017 | 21 | 2017 |
| Quantifying blood pressure visit-to-visit variability in the real-world setting: a retrospective cohort study Y Lu, GC Linderman, S Mahajan, Y Liu, C Huang, R Khera, BJ Mortazavi, ... Circulation: Cardiovascular Quality and Outcomes 16 (4), e009258, 2023 | 20 | 2023 |
| Heterogeneity in early responses in ALLHAT (antihypertensive and lipid-lowering treatment to prevent heart attack trial) SS Dhruva, C Huang, ES Spatz, AC Coppi, F Warner, SX Li, H Lin, X Xu, ... Hypertension 70 (1), 94-102, 2017 | 19 | 2017 |
| Stochastic greedy algorithms for multiple measurement vectors J Qin, S Li, D Needell, A Ma, R Grotheer, C Huang, N Durgin arXiv preprint arXiv:1711.01521, 2017 | 16 | 2017 |
| The National Institutes of Health funding for clinical research applying machine learning techniques in 2017 AR Annapureddy, S Angraal, C Caraballo, A Grimshaw, C Huang, ... NPJ digital medicine 3 (1), 13, 2020 | 14 | 2020 |
| Tracking self-reported symptoms and medical conditions on social media during the COVID-19 pandemic: infodemiological study Q Ding, D Massey, C Huang, CB Grady, Y Lu, A Cohen, P Matzner, ... JMIR public health and surveillance 7 (9), e29413, 2021 | 13 | 2021 |
| Engagement with COVID-19 public health measures in the United States: a cross-sectional social media analysis from June to November 2020 D Massey, C Huang, Y Lu, A Cohen, Y Oren, T Moed, P Matzner, ... Journal of medical Internet research 23 (6), e26655, 2021 | 12 | 2021 |
| Early warning scores with and without artificial intelligence DP Edelson, MM Churpek, KA Carey, Z Lin, C Huang, JM Siner, ... JAMA network open 7 (10), e2438986-e2438986, 2024 | 9 | 2024 |
| Post-Vaccination Syndrome: A Descriptive Analysis of Reported Symptoms and Patient Experiences After Covid-19 Immunization HM Krumholz, Y Wu, M Sawano, R Shah, T Zhou, AS Arun, P Khosla, ... MedRxiv, 2023 | 8 | 2023 |
| Sparse randomized kaczmarz for support recovery of jointly sparse corrupted multiple measurement vectors N Durgin, R Grotheer, C Huang, S Li, A Ma, D Needell, J Qin Research in Data Science, 1-14, 2019 | 7 | 2019 |
