| Do no harm: a roadmap for responsible machine learning for health care J Wiens, S Saria, M Sendak, M Ghassemi, VX Liu, F Doshi-Velez, K Jung, ... Nature medicine 25 (9), 1337-1340, 2019 | 741 | 2019 |
| Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology J Wiens, ES Shenoy Clinical Infectious Diseases, 2017 | 536 | 2017 |
| A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions J Wiens, J Guttag, E Horvitz Journal of the American Medical Informatics Association 21 (4), 699-706, 2014 | 150 | 2014 |
| A framework for effective application of machine learning to microbiome-based classification problems BD Topçuoğlu, NA Lesniak, MT Ruffin IV, J Wiens, PD Schloss MBio 11 (3), 10.1128/mbio. 00434-20, 2020 | 146 | 2020 |
| Patient risk stratification for hospital-associated c. diff as a time-series classification task J Wiens, E Horvitz, J Guttag Advances in neural information processing systems 25, 2012 | 140 | 2012 |
| A Generalizable, Data-Driven Approach to Predict Daily Risk of Clostridium difficile Infection at Two Large Academic Health Centers J Oh, M Makar, C Fusco, R McCaffrey, K Rao, EE Ryan, L Washer, ... Infection Control and Hospital Epidemiology 39 (4), 425-433, 2018 | 133 | 2018 |
| Shapley flow: A graph-based approach to interpreting model predictions J Wang, J Wiens, S Lundberg International Conference on Artificial Intelligence and Statistics, 721-729, 2021 | 108 | 2021 |
| Evaluating a widely implemented proprietary deterioration index model among hospitalized patients with COVID-19 K Singh, TS Valley, S Tang, BY Li, F Kamran, MW Sjoding, J Wiens, ... Annals of the American Thoracic Society 18 (7), 1129-1137, 2021 | 104* | 2021 |
| Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories I Fox, L Ang, M Jaiswal, R Pop-Busui, J Wiens KDD'18 Proceedings of the 24th ACM SIGKDD International Conference on …, 2018 | 101 | 2018 |
| Deep reinforcement learning for closed-loop blood glucose control I Fox, J Lee, R Pop-Busui, J Wiens Machine Learning for Healthcare Conference, 508-536, 2020 | 86* | 2020 |
| Machine learning for patient risk stratification for acute respiratory distress syndrome D Zeiberg, T Prahlad, BK Nallamothu, TJ Iwashyna, J Wiens, MW Sjoding PloS one 14 (3), e0214465, 2019 | 85 | 2019 |
| Patient risk stratification with time-varying parameters: a multitask learning approach J Wiens, J Guttag, E Horvitz Journal of Machine Learning Research 17 (79), 1-23, 2016 | 83 | 2016 |
| Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data S Tang, P Davarmanesh, Y Song, D Koutra, MW Sjoding, J Wiens Journal of the American Medical Informatics Association 27 (12), 1921-1934, 2020 | 81 | 2020 |
| Model selection for offline reinforcement learning: Practical considerations for healthcare settings S Tang, J Wiens Machine Learning for Healthcare Conference, 2-35, 2021 | 78 | 2021 |
| Active learning applied to patient-adaptive heartbeat classification J Wiens, J Guttag Advances in neural information processing systems 23, 2010 | 73 | 2010 |
| Using Machine Learning and the Electronic Health Record to Predict Complicated Clostridium difficile Infection BY Li, J Oh, VB Young, K Rao, J Wiens Open forum infectious diseases 6 (5), ofz186, 2019 | 66 | 2019 |
| Heart sound classification based on temporal alignment techniques JJG Ortiz, CP Phoo, J Wiens 2016 computing in cardiology conference (CinC), 589-592, 2016 | 65 | 2016 |
| Learning Data-Driven Patient Risk Stratification Models for Clostridium difficile J Wiens, WN Campbell, ES Franklin, JV Guttag, E Horvitz Open forum infectious diseases 1 (2), ofu045, 2014 | 65 | 2014 |
| Diagnosing bias in data-driven algorithms for healthcare J Wiens, WN Price, MW Sjoding Nature medicine 26 (1), 25-26, 2020 | 62 | 2020 |
| Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks J Oh, J Wang, J Wiens Proceedings of the 3rd Machine Learning for Health Care (MLHC), 2018 | 61 | 2018 |
Michael W. SjodingUniversity of Michigan在 umich.edu 的电子邮件经过验证
