Search Results for author: Özlem Uzuner

Found 7 papers, 1 papers with code

Ensemble BERT for Classifying Medication-mentioning Tweets

no code implementations SMM4H (COLING) 2020 Huong Dang, Kahyun Lee, Sam Henry, Özlem Uzuner

Twitter is a valuable source of patient-generated data that has been used in various population health studies.

MNLP at FinCausal2022: Nested NER with a Generative Model

no code implementations FNP (LREC) 2022 Jooyeon Lee, Luan Huy Pham, Özlem Uzuner

This paper describes work performed for the FinCasual 2022 Shared Task “Financial Document Causality Detection” (FinCausal 2022).

NER

Prompt-based Extraction of Social Determinants of Health Using Few-shot Learning

no code implementations12 Jun 2023 Giridhar Kaushik Ramachandran, Yujuan Fu, Bin Han, Kevin Lybarger, Nicholas J Dobbins, Özlem Uzuner, Meliha Yetisgen

Social determinants of health (SDOH) documented in the electronic health record through unstructured text are increasingly being studied to understand how SDOH impacts patient health outcomes.

Few-Shot Learning

The 2022 n2c2/UW Shared Task on Extracting Social Determinants of Health

1 code implementation13 Jan 2023 Kevin Lybarger, Meliha Yetisgen, Özlem Uzuner

Results: A total of 15 teams participated, and the top teams utilized pretrained deep learning LM.

Word Embeddings

Extracting Medication Changes in Clinical Narratives using Pre-trained Language Models

no code implementations17 Aug 2022 Giridhar Kaushik Ramachandran, Kevin Lybarger, Yaya Liu, Diwakar Mahajan, Jennifer J. Liang, Ching-Huei Tsou, Meliha Yetisgen, Özlem Uzuner

An accurate and detailed account of patient medications, including medication changes within the patient timeline, is essential for healthcare providers to provide appropriate patient care.

Negation

Performance of Automatic De-identification Across Different Note Types

no code implementations17 Feb 2021 Nicholas Dobbins, David Wayne, Kahyun Lee, Özlem Uzuner, Meliha Yetisgen

Free-text clinical notes detail all aspects of patient care and have great potential to facilitate quality improvement and assurance initiatives as well as advance clinical research.

De-identification

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