Meliha Yetisgen-Yildiz

Also published as: Meliha Yetisgen, Meliha Yetişgen


2020

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Alignment Annotation for Clinic Visit Dialogue to Clinical Note Sentence Language Generation
Wen-wai Yim | Meliha Yetisgen | Jenny Huang | Micah Grossman
Proceedings of the 12th Language Resources and Evaluation Conference

For every patient’s visit to a clinician, a clinical note is generated documenting their medical conversation, including complaints discussed, treatments, and medical plans. Despite advances in natural language processing, automating clinical note generation from a clinic visit conversation is a largely unexplored area of research. Due to the idiosyncrasies of the task, traditional methods of corpus creation are not effective enough approaches for this problem. In this paper, we present an annotation methodology that is content- and technique- agnostic while associating note sentences to sets of dialogue sentences. The sets can further be grouped with higher order tags to mark sets with related information. This direct linkage from input to output decouples the annotation from specific language understanding or generation strategies. Here we provide data statistics and qualitative analysis describing the unique annotation challenges. Given enough annotated data, such a resource would support multiple modeling methods including information extraction with template language generation, information retrieval type language generation, or sequence to sequence modeling.

2017

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Clinical Event Detection with Hybrid Neural Architecture
Adyasha Maharana | Meliha Yetisgen
BioNLP 2017

Event detection from clinical notes has been traditionally solved with rule based and statistical natural language processing (NLP) approaches that require extensive domain knowledge and feature engineering. In this paper, we have explored the feasibility of approaching this task with recurrent neural networks, clinical word embeddings and introduced a hybrid architecture to improve detection for entities with smaller representation in the dataset. A comparative analysis is also done which reveals the complementary behavior of neural networks and conditional random fields in clinical entity detection.

2016

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Annotating and Detecting Medical Events in Clinical Notes
Prescott Klassen | Fei Xia | Meliha Yetisgen
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

Early detection and treatment of diseases that onset after a patient is admitted to a hospital, such as pneumonia, is critical to improving and reducing costs in healthcare. Previous studies (Tepper et al., 2013) showed that change-of-state events in clinical notes could be important cues for phenotype detection. In this paper, we extend the annotation schema proposed in (Klassen et al., 2014) to mark change-of-state events, diagnosis events, coordination, and negation. After we have completed the annotation, we build NLP systems to automatically identify named entities and medical events, which yield an f-score of 94.7% and 91.8%, respectively.

2015

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In-depth annotation for patient level liver cancer staging
Wen-wai Yim | Sharon Kwan | Meliha Yetisgen
Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis

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Annotation of Clinically Important Follow-up Recommendations in Radiology Reports
Meliha Yetisgen | Prescott Klassen | Lucas McCarthy | Elena Pellicer | Tom Payne | Martin Gunn
Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis

2014

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Biomedical/Clinical NLP
Ozlem Uzuner | Meliha Yetişgen | Amber Stubbs
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Tutorial Abstracts

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Annotating Clinical Events in Text Snippets for Phenotype Detection
Prescott Klassen | Fei Xia | Lucy Vanderwende | Meliha Yetisgen
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Early detection and treatment of diseases that onset after a patient is admitted to a hospital, such as pneumonia, is critical to improving and reducing costs in healthcare. NLP systems that analyze the narrative data embedded in clinical artifacts such as x-ray reports can help support early detection. In this paper, we consider the importance of identifying the change of state for events - in particular, clinical events that measure and compare the multiple states of a patient’s health across time. We propose a schema for event annotation comprised of five fields and create preliminary annotation guidelines for annotators to apply the schema. We then train annotators, measure their performance, and finalize our guidelines. With the complete guidelines, we then annotate a corpus of snippets extracted from chest x-ray reports in order to integrate the annotations as a new source of features for classification tasks.

2013

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Annotating Change of State for Clinical Events
Lucy Vanderwende | Fei Xia | Meliha Yetisgen-Yildiz
Workshop on Events: Definition, Detection, Coreference, and Representation

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Identification of Patients with Acute Lung Injury from Free-Text Chest X-Ray Reports
Meliha Yetisgen-Yildiz | Cosmin Bejan | Mark Wurfel
Proceedings of the 2013 Workshop on Biomedical Natural Language Processing

2012

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Statistical Section Segmentation in Free-Text Clinical Records
Michael Tepper | Daniel Capurro | Fei Xia | Lucy Vanderwende | Meliha Yetisgen-Yildiz
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Automatically segmenting and classifying clinical free text into sections is an important first step to automatic information retrieval, information extraction and data mining tasks, as it helps to ground the significance of the text within. In this work we describe our approach to automatic section segmentation of clinical records such as hospital discharge summaries and radiology reports, along with section classification into pre-defined section categories. We apply machine learning to the problems of section segmentation and section classification, comparing a joint (one-step) and a pipeline (two-step) approach. We demonstrate that our systems perform well when tested on three data sets, two for hospital discharge summaries and one for radiology reports. We then show the usefulness of section information by incorporating it in the task of extracting comorbidities from discharge summaries.

2010

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Annotating Large Email Datasets for Named Entity Recognition with Mechanical Turk
Nolan Lawson | Kevin Eustice | Mike Perkowitz | Meliha Yetisgen-Yildiz
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk

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Preliminary Experiments with Amazon’s Mechanical Turk for Annotating Medical Named Entities
Meliha Yetisgen-Yildiz | Imre Solti | Fei Xia | Scott Halgrim
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon’s Mechanical Turk