no code implementations • NAACL (BioNLP) 2021 • Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard, Guergana Savova
We propose a methodology to produce a model focused on the clinical domain: continued pretraining of a model with a broad representation of biomedical terminology (PubMedBERT) on a clinical corpus along with a novel entity-centric masking strategy to infuse domain knowledge in the learning process.
no code implementations • EMNLP (Louhi) 2020 • Kristin Wright-Bettner, Chen Lin, Timothy Miller, Steven Bethard, Dmitriy Dligach, Martha Palmer, James H. Martin, Guergana Savova
We present refinements over existing temporal relation annotations in the Electronic Medical Record clinical narrative.
no code implementations • EMNLP (ClinicalNLP) 2020 • Danielle Bitterman, Timothy Miller, David Harris, Chen Lin, Sean Finan, Jeremy Warner, Raymond Mak, Guergana Savova
We present work on extraction of radiotherapy treatment information from the clinical narrative in the electronic medical records.
no code implementations • NAACL (ClinicalNLP) 2022 • Lijing Wang, Timothy Miller, Steven Bethard, Guergana Savova
In this paper, we investigate ensemble methods for fine-tuning transformer-based pretrained models for clinical natural language processing tasks, specifically temporal relation extraction from the clinical narrative.
no code implementations • NAACL (ClinicalNLP) 2022 • Dmitriy Dligach, Steven Bethard, Timothy Miller, Guergana Savova
Sequence-to-sequence models are appealing because they allow both encoder and decoder to be shared across many tasks by formulating those tasks as text-to-text problems.
1 code implementation • 18 Oct 2023 • Sheng Lu, Shan Chen, Yingya Li, Danielle Bitterman, Guergana Savova, Iryna Gurevych
In-context learning (ICL) is a new learning paradigm that has gained popularity along with the development of large language models.
no code implementations • WS 2020 • Chen Lin, Timothy Miller, Dmitriy Dligach, Farig Sadeque, Steven Bethard, Guergana Savova
Recently BERT has achieved a state-of-the-art performance in temporal relation extraction from clinical Electronic Medical Records text.
1 code implementation • 24 Jun 2020 • Cindy Li, Elizabeth Chen, Guergana Savova, Hamish Fraser, Carsten Eickhoff
Diagnostic errors can pose a serious threat to patient safety, leading to serious harm and even death.
no code implementations • 24 Jun 2020 • Gil Alon, Elizabeth Chen, Guergana Savova, Carsten Eickhoff
Scores fell from 0. 28 for the 50 most prevalent ICD-9-CM codes to 0. 03 for the 1000 most prevalent ICD-9-CM codes.
no code implementations • WS 2019 • Kristin Wright-Bettner, Martha Palmer, Guergana Savova, Piet de Groen, Timothy Miller
This paper discusses a cross-document coreference annotation schema that was developed to further automatic extraction of timelines in the clinical domain.
no code implementations • WS 2019 • Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard, Guergana Savova
Classic methods for clinical temporal relation extraction focus on relational candidates within a sentence.
no code implementations • WS 2018 • Chen Lin, Timothy Miller, Dmitriy Dligach, Hadi Amiri, Steven Bethard, Guergana Savova
Neural network models are oftentimes restricted by limited labeled instances and resort to advanced architectures and features for cutting edge performance.
no code implementations • NAACL 2018 • Hadi Amiri, Timothy Miller, Guergana Savova
Automatic identification of spurious instances (those with potentially wrong labels in datasets) can improve the quality of existing language resources, especially when annotations are obtained through crowdsourcing or automatically generated based on coded rankings.
no code implementations • EMNLP 2017 • Hadi Amiri, Timothy Miller, Guergana Savova
We present a novel approach for training artificial neural networks.
no code implementations • WS 2017 • Timothy Miller, Steven Bethard, Hadi Amiri, Guergana Savova
Detecting negated concepts in clinical texts is an important part of NLP information extraction systems.
no code implementations • WS 2017 • Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard, Guergana Savova
Token sequences are often used as the input for Convolutional Neural Networks (CNNs) in natural language processing.
no code implementations • SEMEVAL 2017 • Steven Bethard, Guergana Savova, Martha Palmer, James Pustejovsky
Clinical TempEval 2017 aimed to answer the question: how well do systems trained on annotated timelines for one medical condition (colon cancer) perform in predicting timelines on another medical condition (brain cancer)?
no code implementations • EACL 2017 • Dmitriy Dligach, Timothy Miller, Chen Lin, Steven Bethard, Guergana Savova
We experiment with neural architectures for temporal relation extraction and establish a new state-of-the-art for several scenarios.
no code implementations • TACL 2014 • William F. Styler IV, Steven Bethard, Sean Finan, Martha Palmer, Sameer Pradhan, Piet C de Groen, Brad Erickson, Timothy Miller, Chen Lin, Guergana Savova, James Pustejovsky
The corpus is available to the community and has been proposed for use in a SemEval 2015 task.