| Evaluating large language models as agents in the clinic N Mehandru, BY Miao, ER Almaraz, M Sushil, AJ Butte, A Alaa NPJ digital medicine 7 (1), 84, 2024 | 56* | 2024 |
| Use of a large language model to assess clinical acuity of adults in the emergency department CYK Williams, T Zack, BY Miao, M Sushil, M Wang, AE Kornblith, AJ Butte JAMA Network Open 7 (5), e248895-e248895, 2024 | 50* | 2024 |
| Patient representation learning and interpretable evaluation using clinical notes M Sushil, S Šuster, K Luyckx, W Daelemans Journal of biomedical informatics 84, 103-113, 2018 | 50 | 2018 |
| CORAL: expert-curated oncology reports to advance language model inference M Sushil, VE Kennedy, D Mandair, BY Miao, T Zack, AJ Butte Nejm Ai 1 (4), AIdbp2300110, 2024 | 35 | 2024 |
| Are we there yet? Exploring clinical domain knowledge of BERT models M Sushil, S Suster, W Daelemans Proceedings of the 20th workshop on biomedical language processing, 41-53, 2021 | 31 | 2021 |
| Counting trees in random forests: predicting symptom severity in psychiatric intake reports E Scheurwegs, M Sushil, S Tulkens, W Daelemans, K Luyckx Journal of biomedical informatics 75, S112-S119, 2017 | 28 | 2017 |
| Rule induction for global explanation of trained models M Sushil, S Šuster, W Daelemans Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and …, 2018 | 27 | 2018 |
| A comparative study of large language model-based zero-shot inference and task-specific supervised classification of breast cancer pathology reports M Sushil, T Zack, D Mandair, Z Zheng, A Wali, YN Yu, Y Quan, D Lituiev, ... Journal of the American Medical Informatics Association 31 (10), 2315-2327, 2024 | 16* | 2024 |
| Bottom-up and top-down paradigms of artificial intelligence research approaches to healthcare data science using growing real-world big data M Wang, M Sushil, BY Miao, AJ Butte Journal of the American Medical Informatics Association 30 (7), 1323-1332, 2023 | 13 | 2023 |
| Characterisation of digital therapeutic clinical trials: a systematic review with natural language processing BY Miao, M Sushil, A Xu, M Wang, D Arneson, E Berkley, M Subash, ... The Lancet Digital Health 6 (3), e222-e229, 2024 | 12 | 2024 |
| Developing a general-purpose clinical language inference model from a large corpus of clinical notes M Sushil, D Ludwig, AJ Butte, VA Rudrapatna arXiv preprint arXiv:2210.06566, 2022 | 12 | 2022 |
| Topic modeling on clinical social work notes for exploring social determinants of health factors S Sun, T Zack, CYK Williams, M Sushil, AJ Butte JAMIA open 7 (1), ooad112, 2024 | 10 | 2024 |
| Integration of Safety and Smartness Using Cloud Services: An Insight to Future N Tekriwal, Madhumita, PV Krishna Innovations and Advances in Computer, Information, Systems Sciences, and …, 2013 | 10 | 2013 |
| Algorithmic Identification of Treatment‐Emergent Adverse Events From Clinical Notes Using Large Language Models: A Pilot Study in Inflammatory Bowel Disease AL Silverman, M Sushil, B Bhasuran, D Ludwig, J Buchanan, R Racz, ... Clinical Pharmacology & Therapeutics 115 (6), 1391-1399, 2024 | 8 | 2024 |
| Biomedical large languages models seem not to be superior to generalist models on unseen medical data FJ Dorfner, A Dada, F Busch, MR Makowski, T Han, D Truhn, J Kleesiek, ... arXiv preprint arXiv:2408.13833, 2024 | 7 | 2024 |
| Contextual explanation rules for neural clinical classifiers M Sushil, S Suster, W Daelemans Proceedings of the 20th Workshop on Biomedical Language Processing, 202-212, 2021 | 5* | 2021 |
| Revisiting neural relation classification in clinical notes with external information S Šuster, M Sushil, W Daelemans Workshop on Health Text Mining and Information Analysis (LOUHI), workshop at …, 2018 | 4 | 2018 |
| The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN) BY Miao, IY Chen, CYK Williams, J Davidson, A Garcia-Agundez, S Sun, ... arXiv preprint arXiv:2403.02558, 2024 | 3 | 2024 |
| Abstract B074: Clinical inference of location and trajectory of pancreatic cancer from radiology reports using zero-shot LLM TI Zack, M Sushil, B Miao, A Demirci, C Kasap, M Tempero, A Butte, ... Cancer Research 84 (2_Supplement), B074-B074, 2024 | 2 | 2024 |
| Unsupervised patient representations from clinical notes with interpretable classification decisions M Sushil, S Šuster, K Luyckx, W Daelemans Workshop on Machine Learning for Health, NeurIPS 2017, 2017 | 2 | 2017 |
Walter DaelemansProfessor of Computational Linguistics, University of AntwerpAdresse e-mail validée de uantwerpen.be
