Search Results for author: Guergana Savova

Found 28 papers, 1 papers with code

EntityBERT: Entity-centric Masking Strategy for Model Pretraining for the Clinical Domain

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.

Natural Language Processing Negation Detection +1

Diagnosis Prevalence vs. Efficacy in Machine-learning Based Diagnostic Decision Support

no code implementations24 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.

Mining Misdiagnosis Patterns from Biomedical Literature

1 code implementation24 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.

Cross-document coreference: An approach to capturing coreference without context

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.

Spotting Spurious Data with Neural Networks

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.

SemEval-2017 Task 12: Clinical TempEval

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)?

Domain Adaptation Temporal Information Extraction

Neural Temporal Relation Extraction

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.

Relation Classification Temporal Information Extraction

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