| Publicly available clinical BERT embeddings E Alsentzer, JR Murphy, W Boag, WH Weng, D Jin, T Naumann, ... arXiv preprint arXiv:1904.03323, 2019 | 1525 | 2019 |
| Domain-specific language model pretraining for biomedical natural language processing Y Gu, R Tinn, H Cheng, M Lucas, N Usuyama, X Liu, T Naumann, J Gao, ... ACM Transactions on Computing for Healthcare (HEALTH) 3 (1), 1-23, 2021 | 999 | 2021 |
| Unfolding physiological state: Mortality modelling in intensive care units M Ghassemi, T Naumann, F Doshi-Velez, N Brimmer, R Joshi, ... Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014 | 268 | 2014 |
| A multivariate timeseries modeling approach to severity of illness assessment and forecasting in ICU with sparse, heterogeneous clinical data M Ghassemi, M Pimentel, T Naumann, T Brennan, D Clifton, P Szolovits, ... Proceedings of the AAAI conference on artificial intelligence 29 (1), 2015 | 255 | 2015 |
| A review of challenges and opportunities in machine learning for health M Ghassemi, T Naumann, P Schulam, AL Beam, IY Chen, R Ranganath AMIA Summits on Translational Science Proceedings 2020, 191, 2020 | 221 | 2020 |
| Secondary analysis of electronic health records MIT Critical Data Springer Nature, 2016 | 170 | 2016 |
| Predicting early psychiatric readmission with natural language processing of narrative discharge summaries A Rumshisky, M Ghassemi, T Naumann, P Szolovits, VM Castro, ... Translational psychiatry 6 (10), e921-e921, 2016 | 165 | 2016 |
| Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii S Wang, MBA McDermott, G Chauhan, M Ghassemi, MC Hughes, ... Proceedings of the ACM conference on health, inference, and learning, 222-235, 2020 | 147 | 2020 |
| Natural language processing for EHR-based computational phenotyping Z Zeng, Y Deng, X Li, T Naumann, Y Luo IEEE/ACM transactions on computational biology and bioinformatics 16 (1 …, 2018 | 138 | 2018 |
| Feature robustness in non-stationary health records: caveats to deployable model performance in common clinical machine learning tasks B Nestor, MBA McDermott, W Boag, G Berner, T Naumann, MC Hughes, ... Machine Learning for Healthcare Conference, 381-405, 2019 | 109 | 2019 |
| The role of machine learning in clinical research: transforming the future of evidence generation EH Weissler, T Naumann, T Andersson, R Ranganath, O Elemento, Y Luo, ... Trials 22 (1), 1-15, 2021 | 91 | 2021 |
| Making big data useful for health care: a summary of the inaugural mit critical data conference O Badawi, T Brennan, LA Celi, M Feng, M Ghassemi, A Ippolito, ... JMIR medical informatics 2 (2), e3447, 2014 | 86 | 2014 |
| Opportunities in machine learning for healthcare M Ghassemi, T Naumann, P Schulam, AL Beam, R Ranganath arXiv preprint arXiv:1806.00388, 2018 | 78 | 2018 |
| Practical guidance on artificial intelligence for health-care data M Ghassemi, T Naumann, P Schulam, AL Beam, IY Chen, R Ranganath The Lancet Digital Health 1 (4), e157-e159, 2019 | 73 | 2019 |
| A “datathon” model to support cross-disciplinary collaboration J Aboab, LA Celi, P Charlton, M Feng, M Ghassemi, DC Marshall, ... Science Translational Medicine 8 (333), 333ps8-333ps8, 2016 | 66 | 2016 |
| What’s in a note? unpacking predictive value in clinical note representations W Boag, D Doss, T Naumann, P Szolovits AMIA Summits on Translational Science Proceedings 2018, 26, 2018 | 62 | 2018 |
| Making the most of text semantics to improve biomedical vision–language processing B Boecking, N Usuyama, S Bannur, DC Castro, A Schwaighofer, S Hyland, ... European conference on computer vision, 1-21, 2022 | 53 | 2022 |
| Trends and focus of machine learning applications for health research B Beaulieu-Jones, SG Finlayson, C Chivers, I Chen, M McDermott, ... JAMA network open 2 (10), e1914051-e1914051, 2019 | 49 | 2019 |
| CliNER: A lightweight tool for clinical named entity recognition W Boag, K Wacome, T Naumann, A Rumshisky AMIA joint summits on clinical research informatics (poster), 2015 | 48 | 2015 |
| Predicting clinical outcomes across changing electronic health record systems JJ Gong, T Naumann, P Szolovits, JV Guttag Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017 | 45 | 2017 |
Dr. Marzyeh GhassemiAssistant Professor, EECS.CSAIL/IMES, MITVerified email at mit.edu
