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Proceedings of the Conference. Association for Computational Linguistics. Meeting|April 28, 2021
Two-stage Federated Phenotyping and Patient Representation LearningDianbo Liu, Dmitriy Dligach, Timothy Miller
Journal of Biomedical Informatics|December 1, 2020
Pre-training phenotyping classifiersDmitriy Dligach, Majid Afshar, Timothy Miller
Proceedings of the Conference. Association for Computational Linguistics. North American Chapter. Meeting|May 24, 2021
Extracting Adverse Drug Event Information with Minimal EngineeringTimothy Miller, Alon Geva, Dmitriy Dligach
Journal of the American Medical Informatics Association : JAMIA|June 25, 2019
Toward a clinical text encoder: pretraining for clinical natural language processing with applications to substance misuseDmitriy Dligach, Majid Afshar, Timothy Miller
AMIA ... Annual Symposium Proceedings. AMIA Symposium|March 10, 2016
Semi-supervised Learning for Phenotyping TasksDmitriy Dligach, Timothy Miller, Guergana K Savova
Proceedings of the Conference. Association for Computational Linguistics. Meeting|October 2, 2023
End-to-end clinical temporal information extraction with multi-head attentionTimothy Miller, Steven Bethard, Dmitriy Dligach, et al.
Journal of Biomedical Informatics|April 26, 2017
Towards generalizable entity-centric clinical coreference resolutionTimothy Miller, Dmitriy Dligach, Steven Bethard, et al.
Journal of the American Medical Informatics Association : JAMIA|October 5, 2013
Discovering body site and severity modifiers in clinical textsDmitriy Dligach, Steven Bethard, Lee Becker, et al.
Proceedings of the Conference. Association for Computational Linguistics. Meeting|August 16, 2023
Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients' Active Diagnoses and Problems from Electronic Health Record Progress NotesYanjun Gao, Dmitriy Dligach, Timothy Miller, et al.
Proceedings of COLING. International Conference on Computational Linguistics|October 21, 2022
Summarizing Patients' Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence ModelsYanjun Gao, Timothy Miller, Dongfang Xu, et al.
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