Publications

Loyola at ArchEHR-QA 2025: Exploring Unsupervised Attribution of Generated Text: Attention and Clustering-Based Methods

Published in Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks), 2025

Recommended citation:

Rohan Sethi, Timothy Miller, Majid Afshar, and Dmitriy Dligach. 2025. Loyola at ArchEHR-QA 2025: Exploring Unsupervised Attribution of Generated Text: Attention and Clustering-Based Methods. In Proceedings of the 24th Workshop on Biomedical Language Processing (Shared Tasks), pages 22–26, Vienna, Austria. Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.bionlp-share.3

Do They Really Know? Evaluating Large Language Models’ Ability to Reference and Cite Oncology Guidelines

Published in International Conference on Artificial Intelligence in Medicine, 2025

Recommended citation:

Belligoli, P., Bitterman, D., Miller, T. (2025). Do They Really Know? Evaluating Large Language Models’ Ability to Reference and Cite Oncology Guidelines. In: Bellazzi, R., Juarez Herrero, J.M., Sacchi, L., Zupan, B. (eds) Artificial Intelligence in Medicine. AIME 2025. Lecture Notes in Computer Science(), vol 15735. Springer, Cham. https://doi.org/10.1007/978-3-031-95841-0_6 https://doi.org/10.1007/978-3-031-95841-0_6

Uncertainty estimation in diagnosis generation from large language models: next-word probability is not pre-test probability

Published in JAMIA Open, 2025

Recommended citation:

Yanjun Gao, Skatje Myers, Shan Chen, Dmitriy Dligach, Timothy Miller, Danielle S Bitterman, Guanhua Chen, Anoop Mayampurath, Matthew M Churpek, Majid Afshar, Uncertainty estimation in diagnosis generation from large language models: next-word probability is not pre-test probability, JAMIA Open, Volume 8, Issue 1, February 2025, ooae154, https://doi.org/10.1093/jamiaopen/ooae154 https://doi.org/10.1093/jamiaopen/ooae154

Lessons learned on information retrieval in electronic health records: a comparison of embedding models and pooling strategies

Published in JAMIA, 2024

Recommended citation:

Skatje Myers, Timothy A Miller, Yanjun Gao, Matthew M Churpek, Anoop Mayampurath, Dmitriy Dligach, Majid Afshar, Lessons learned on information retrieval in electronic health records: a comparison of embedding models and pooling strategies, Journal of the American Medical Informatics Association, Volume 32, Issue 2, February 2025, Pages 357–364, https://doi.org/10.1093/jamia/ocae308 https://doi.org/10.1093/jamia/ocae308

LCD benchmark: long clinical document benchmark on mortality prediction for language models

Published in Journal of the American Medical Informatics Association, 2024

Recommended citation:

WonJin Yoon, Shan Chen, Yanjun Gao, Zhanzhan Zhao, Dmitriy Dligach, Danielle S Bitterman, Majid Afshar, Timothy Miller, LCD benchmark: long clinical document benchmark on mortality prediction for language models. Journal of the American Medical Informatics Association, 2024, ocae287, https://doi.org/10.1093/jamia/ocae287 https://doi.org/10.1093/jamia/ocae287

Cumulus: a federated electronic health record-based learning system powered by Fast Healthcare Interoperability Resources and artificial intelligence

Published in Journal of the American Medical Informatics Association, 2024

Recommended citation:

Andrew J McMurry, Daniel I Gottlieb, Timothy A Miller, James R Jones, Ashish Atreja, Jennifer Crago, Pankaja M Desai, Brian E Dixon, Matthew Garber, Vladimir Ignatov, Lyndsey A Kirchner, Philip R O Payne, Anil J Saldanha, Prabhu R V Shankar, Yauheni V Solad, Elizabeth A Sprouse, Michael Terry, Adam B Wilcox, Kenneth D Mandl, Cumulus: a federated electronic health record-based learning system powered by Fast Healthcare Interoperability Resources and artificial intelligence, Journal of the American Medical Informatics Association, Volume 31, Issue 8, August 2024, Pages 1638–1647, https://doi.org/10.1093/jamia/ocae130 https://doi.org/10.1093/jamia/ocae130

Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models

Published in JAMIA, 2024

Recommended citation:

Jifan Gao, Guanhua Chen, Ann P O’Rourke, John Caskey, Kyle A Carey, Madeline Oguss, Anne Stey, Dmitriy Dligach, Timothy Miller, Anoop Mayampurath, Matthew M Churpek, Majid Afshar, Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models, Journal of the American Medical Informatics Association, Volume 31, Issue 6, June 2024, Pages 1291–1302, https://doi.org/10.1093/jamia/ocae071 https://doi.org/10.1093/jamia/ocae071

Development of a Benchmark Corpus for Medical Device Adverse Event Detection

Published in CL4Health Workshop, 2024

Recommended citation:

Susmitha Wunnava, David A. Harris, Florence T. Bourgeois, and Timothy A. Miller. 2024. Development of a Benchmark Corpus for Medical Device Adverse Event Detection. In Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024, pages 240–245, Torino, Italia. ELRA and ICCL. https://aclanthology.org/2024.cl4health-1.29

Improving Model Transferability for Clinical Note Section Classification Models Using Continued Pretraining

Published in Journal of the American Medical Informatics Association (JAMIA), 2023

Recommended citation:

Weipeng Zhou, Meliha Yetisgen, Yanjun Gao, Guergana Savova, and Timothy Miller. 2023. Improving Model Transferability for Clinical Note Section Classification Models Using Continued Pretraining. JAMIA, September 2023, ocad190 https://academic.oup.com/jamia/advance-article/doi/10.1093/jamia/ocad190/7277369?login=true

Improving the Transferability of Clinical Note Section Classification Models with BERT and Large Language Model Ensembles

Published in Proceedings of the 5th Clinical Natural Language Processing Workshop, 2023

Recommended citation:

Weipeng Zhou, Majid Afshar, Dmitriy Dligach, Yanjun Gao, and Timothy Miller. 2023. Improving the Transferability of Clinical Note Section Classification Models with BERT and Large Language Model Ensembles. In Proceedings of the 5th Clinical Natural Language Processing Workshop, pages 125–130, Toronto, Canada. Association for Computational Linguistics. https://aclanthology.org/2023.clinicalnlp-1.16/

Two-Stage Fine-Tuning for Improved Bias and Variance for Large Pretrained Language Models

Published in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

Recommended citation:

Lijing Wang, Yingya Li, Timothy Miller, Steven Bethard, and Guergana Savova. 2023. Two-Stage Fine-Tuning for Improved Bias and Variance for Large Pretrained Language Models. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15746–15761, Toronto, Canada. Association for Computational Linguistics. https://aclanthology.org/2023.acl-long.877/

End-to-end clinical temporal information extraction with multi-head attention

Published in The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, 2023

Recommended citation:

Timothy Miller, Steven Bethard, Dmitriy Dligach, and Guergana Savova. 2023. End-to-end clinical temporal information extraction with multi-head attention. In The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pages 313–319, Toronto, Canada. Association for Computational Linguistics. https://aclanthology.org/2023.bionlp-1.28/

Representing and utilizing clinical textual data for real world studies: An OHDSI approach

Published in Journal of Biomedical Informatics, 2023

Recommended citation:

Vipina K. Keloth, Juan M. Banda, Michael Gurley, Paul M. Heider, Georgina Kennedy, Hongfang Liu, Feifan Liu, Timothy Miller, Karthik Natarajan, Olga V Patterson, Yifan Peng, Kalpana Raja, Ruth M. Reeves, Masoud Rouhizadeh, Jianlin Shi, Xiaoyan Wang, Yanshan Wang, Wei-Qi Wei, Andrew E. Williams, Rui Zhang, Rimma Belenkaya, Christian Reich, Clair Blacketer, Patrick Ryan, George Hripcsak, Noémie Elhadad, Hua Xu, Representing and utilizing clinical textual data for real world studies: An OHDSI approach, Journal of Biomedical Informatics, Volume 142, 2023 https://doi.org/10.1016/j.jbi.2023.104343

Natural Language Processing Methods to Empirically Explore Social Contexts and Needs in Cancer Patient Notes

Published in JCO Clinical Cancer Informatics, 2023

Recommended citation:

Natural Language Processing Methods to Empirically Explore Social Contexts and Needs in Cancer Patient Notes. Abigail Derton, Marco Guevara, Shan Chen, Shalini Moningi, David E. Kozono, Dianbo Liu, Timothy A. Miller, Guergana K. Savova, Raymond H. Mak, and Danielle S. Bitterman. JCO Clinical Cancer Informatics 2023 :7