| Publicly available clinical BERT embeddings E Alsentzer, JR Murphy, W Boag, WH Weng, D Jin, T Naumann, ... arXiv preprint arXiv:1904.03323, 2019 | 2099 | 2019 |
| Clinically accurate chest x-ray report generation G Liu, TMH Hsu, M McDermott, W Boag, WH Weng, P Szolovits, ... Machine Learning for Healthcare Conference, 249-269, 2019 | 261 | 2019 |
| 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 | 137 | 2019 |
| 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 | 69 | 2018 |
| Baselines for chest x-ray report generation W Boag, TMH Hsu, M McDermott, G Berner, E Alesentzer, P Szolovits Machine learning for health workshop, 126-140, 2020 | 58 | 2020 |
| 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 | 49 | 2015 |
| Unsupervised multimodal representation learning across medical images and reports TMH Hsu, WH Weng, W Boag, M McDermott, P Szolovits arXiv preprint arXiv:1811.08615, 2018 | 44 | 2018 |
| Cliner 2.0: Accessible and accurate clinical concept extraction W Boag, E Sergeeva, S Kulshreshtha, P Szolovits, A Rumshisky, ... arXiv preprint arXiv:1803.02245, 2018 | 38 | 2018 |
| Twitterhawk: A feature bucket based approach to sentiment analysis W Boag, P Potash, A Rumshisky Proceedings of the 9th International Workshop on Semantic Evaluation …, 2015 | 24 | 2015 |
| Proceedings of the 2nd Clinical Natural Language Processing Workshop E Alsentzer, J Murphy, W Boag, WH Weng, D Jindi, T Naumann, ... Minneapolis, MN, 2019 | 23 | 2019 |
| Tech worker organizing for power and accountability W Boag, H Suresh, B Lepe, C D'Ignazio Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 20 | 2022 |
| Racial disparities and mistrust in end-of-life care W Boag, H Suresh, LA Celi, P Szolovits, M Ghassemi Machine learning for healthcare conference, 587-602, 2018 | 20 | 2018 |
| Organizational governance of emerging technologies: AI adoption in healthcare JY Kim, W Boag, F Gulamali, A Hasan, HDJ Hogg, M Lifson, D Mulligan, ... proceedings of the 2023 ACM conference on fairness, accountability, and …, 2023 | 19 | 2023 |
| Hard for humans, hard for machines: predicting readmission after psychiatric hospitalization using narrative notes W Boag, O Kovaleva, TH McCoy Jr, A Rumshisky, P Szolovits, RH Perlis Translational psychiatry 11 (1), 32, 2021 | 18 | 2021 |
| Awe-cm vectors: Augmenting word embeddings with a clinical metathesaurus W Boag, H Kané arXiv preprint arXiv:1712.01460, 2017 | 13 | 2017 |
| Clinical collabsheets: 53 questions to guide a clinical collaboration S Saleh, W Boag, L Erdman, T Naumann Machine Learning for Healthcare Conference, 783-812, 2020 | 10 | 2020 |
| A Hybrid Approach to Precision Medicine-related Biomedical Article Retrieval and Clinical Trial Matching. Y Ling, SA Hasan, M Filannino, KP Buchan, K Lee, J Liu, W Boag, D Jin, ... TREC, 2017 | 9 | 2017 |
| Simihawk at semeval-2016 task 1: A deep ensemble system for semantic textual similarity P Potash, W Boag, A Romanov, V Ramanishka, A Rumshisky Proceedings of the 10th International Workshop on Semantic Evaluation …, 2016 | 9 | 2016 |
| A Pilot Study in Surveying Clinical Judgments to Evaluate Radiology Report Generation W Boag, H Kané, S Rawat, J Wei, A Goehler Proceedings of the 2021 ACM Conference on Fairness, Accountability, and …, 2021 | 7 | 2021 |
| EHR safari: data is contextual W Boag, M Oladipo, P Szolovits Machine Learning for Healthcare Conference, 391-408, 2022 | 6 | 2022 |
Tristan NaumannPrincipal Researcher, Microsoft Research Health Futures在 microsoft.com 的电子邮件经过验证
