W. Boag. Quantifying Racial Disparities in End-of-Life Care. MIT. Master’s Thesis. June, 2018. [repo] [slides] [blog]
W. Boag. Sense-Aware Word Embeddings Using Stream Clustering. UMass Lowell. Bachelor’s Honors Thesis. May 2016.
Refereed Conference Papers
G. Liu, T.M. H. Hsu, M. McDermott, W. Boag, W.H. Weng, P. Szolovits, M. Ghassemi. Clinically Accurate Chest X-Ray Report Generation. Machine Learning for Healthcare (MLHC 2019). August, 2019. Ann Arbor, MI. [poster]
B. Nestor, M. McDermott, W. Boag, G. Berner, T. Naumann, M. C. Hughes, A. Goldenberg, M. Ghassemi. Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks. Machine Learning for Healthcare (MLHC 2019). August, 2019. Ann Arbor, MI. [repo] [poster] [slides]
W. Boag, H. Suresh, L.A. Celi, P. Szolovits, M. Ghassemi. Racial Disparities and Mistrust in End-of-Life Care. Machine Learning for Healthcare (MLHC 2018). August, 2018. Palo Alto, CA. [repo] [slides] [blog]
W. Boag, D. Doss, T. Naumann, P. Szolovits. What’s in a Note? Unpacking Predictive Value in Clinical Note Representations. AMIA 2018 Informatics Summit. March, 2018. San Francisco, California. [repo] [slides]
W. Boag, R. Campos, K. Saenko, A. Rumshisky. MUTT: Metric Unit TesTing for Language Generation Tasks.ACL 2016. August, 2016.Berlin, Germany. [repo][poster]
T.M. H. Hsu, W.H.Weng, W. Boag, M. McDermott, P. Szolovits. Unsupervised Multimodal Representation Learning across Medical Images and Reports. NeurIPS 2018 Workshop on Machine Learning for Health. December, 2018. Montreal, Canada.
W. Boag, H. Suresh, L.A. Celi, P. Szolovits, M. Ghassemi. Modeling Mistrust in End-of-Life Care. Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2018). July, 2018. Stockholm, Sweden. [repo][poster]
W. Boag, M. Kane, P. Szolovits. AWE-CM Vectors: Augmenting Word Embeddings with a Clinical Metathesaurus. NeurIPS 2017 Workshop on Machine Learning for Health. December, 2017. Long Beach, California. [repo] [poster]
W. Boag, T. Naumann, E. Sergeeva, S. Kulshreshtha, P. Szolovits, A. Rumshisky. CliNER 2.0: Accessible and Accurate Clinical Concept Extraction. NeurIPS 2017 Workshop on Machine Learning for Health. December, 2017. Long Beach, California. [repo] [poster]
Y. Ling, S. Hasan, M. Filannino, K. Buchan, K. Lee, J. Liu, W. Boag, D. Jin, O. Uzuner, K. Lee, V. Datla, A. Qadir, D. Farri. A Hybrid Approach to Precision Medicine-related Biomedical Article Retrieval and Clinical Trial Matching. TREC 2017 Precision Medicine / Clinical Decision Support Track. November, 2017. Gaithersburg, Maryland.
W. Boag, T. Naumann, P. Szolovits. Towards the Creation of a Large Corpus of Synthetically-Identified Clinical Notes.NeurIPS 2016 Workshop on Machine Learning for Health. December, 2016. Barcelona, Spain. [repo][poster]
P. Potash, W. Boag, A. Romanov, V. Ramanishka, A. Rumshisky. Simihawk at Semeval 2016 Task 1: A Deep Ensemble System for Semantic Textual Similairty. The 10th International Workshop on Semantic Evaluation (SemEval-2016). NAACL HLT 2016. June, 2016. San Diego, California. [poster]2
W. Boag, P. Potash, A. Rumshisky. TwitterHawk: A Feature Bucket Approach to Sentiment Analysis, In Proceedings of the 9th international workshop on Semantic Evaluation Exercises (SemEval 2015). June 2015, Denver, Colorado, USA. [repo] [poster]
W. Boag, K. Wacome, T. Naumann, A. Rumshisky. CliNER: A Lightweight Tool for Clinical Concept Extraction (abstract). AMIA Joint Summits on Clinical Research Informatics (AMIA CRI 2015). San Francisco, CA. [repo] [poster]