| Publicly available clinical BERT embeddings E Alsentzer, JR Murphy, W Boag, WH Weng, D Jin, T Naumann, ... arXiv preprint arXiv:1904.03323, 2019 | 2518 | 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 | 2132 | 2021 |
| Llava-med: Training a large language-and-vision assistant for biomedicine in one day C Li, C Wong, S Zhang, N Usuyama, H Liu, J Yang, T Naumann, H Poon, ... Advances in Neural Information Processing Systems 36, 2024 | 622 | 2024 |
| 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 | 395 | 2020 |
| Secondary analysis of electronic health records MIT Critical Data Springer Nature, 2016 | 308 | 2016 |
| 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 | 293 | 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 | 288 | 2015 |
| 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, 1-15, 2021 | 269 | 2021 |
| 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 | 266 | 2020 |
| 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 | 257 | 2022 |
| 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 | 210 | 2016 |
| 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 | 195 | 2018 |
| A whole-slide foundation model for digital pathology from real-world data H Xu, N Usuyama, J Bagga, S Zhang, R Rao, T Naumann, C Wong, ... Nature, 1-8, 2024 | 183 | 2024 |
| Large-scale domain-specific pretraining for biomedical vision-language processing S Zhang, Y Xu, N Usuyama, J Bagga, R Tinn, S Preston, R Rao, M Wei, ... arXiv preprint arXiv:2303.00915 2 (3), 6, 2023 | 164 | 2023 |
| 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 | 159 | 2019 |
| BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs S Zhang, Y Xu, N Usuyama, H Xu, J Bagga, R Tinn, S Preston, R Rao, ... arXiv preprint arXiv:2303.00915, 2023 | 154 | 2023 |
| Fine-tuning large neural language models for biomedical natural language processing R Tinn, H Cheng, Y Gu, N Usuyama, X Liu, T Naumann, J Gao, H Poon Patterns 4 (4), 2023 | 123 | 2023 |
| 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 | 104 | 2019 |
| 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 | 98 | 2014 |
| Opportunities in machine learning for healthcare M Ghassemi, T Naumann, P Schulam, AL Beam, R Ranganath arXiv preprint arXiv:1806.00388, 2018 | 92 | 2018 |
Hoifung PoonGeneral Manager, Microsoft Health FuturesZweryfikowany adres z microsoft.com
