Literature DB >> 26433122

Creation of a new longitudinal corpus of clinical narratives.

Vishesh Kumar1, Amber Stubbs2, Stanley Shaw3, Özlem Uzuner4.   

Abstract

The 2014 i2b2/UTHealth Natural Language Processing (NLP) shared task featured a new longitudinal corpus of 1304 records representing 296 diabetic patients. The corpus contains three cohorts: patients who have a diagnosis of coronary artery disease (CAD) in their first record, and continue to have it in subsequent records; patients who do not have a diagnosis of CAD in the first record, but develop it by the last record; patients who do not have a diagnosis of CAD in any record. This paper details the process used to select records for this corpus and provides an overview of novel research uses for this corpus. This corpus is the only annotated corpus of longitudinal clinical narratives currently available for research to the general research community.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Corpus; Machine learning; Medical records; NLP

Mesh:

Year:  2015        PMID: 26433122      PMCID: PMC4978168          DOI: 10.1016/j.jbi.2015.09.018

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  16 in total

Review 1.  Evaluating the state of the art in coreference resolution for electronic medical records.

Authors:  Ozlem Uzuner; Andreea Bodnari; Shuying Shen; Tyler Forbush; John Pestian; Brett R South
Journal:  J Am Med Inform Assoc       Date:  2012-02-24       Impact factor: 4.497

2.  Extracting medication information from clinical text.

Authors:  Ozlem Uzuner; Imre Solti; Eithon Cadag
Journal:  J Am Med Inform Assoc       Date:  2010 Sep-Oct       Impact factor: 4.497

3.  Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus.

Authors:  Amber Stubbs; Özlem Uzuner
Journal:  J Biomed Inform       Date:  2015-08-28       Impact factor: 6.317

4.  Coronary artery disease risk assessment from unstructured electronic health records using text mining.

Authors:  Jitendra Jonnagaddala; Siaw-Teng Liaw; Pradeep Ray; Manish Kumar; Nai-Wen Chang; Hong-Jie Dai
Journal:  J Biomed Inform       Date:  2015-08-28       Impact factor: 6.317

5.  Recognizing obesity and comorbidities in sparse data.

Authors:  Ozlem Uzuner
Journal:  J Am Med Inform Assoc       Date:  2009-04-23       Impact factor: 4.497

6.  Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions.

Authors:  Wendy W Chapman; Prakash M Nadkarni; Lynette Hirschman; Leonard W D'Avolio; Guergana K Savova; Ozlem Uzuner
Journal:  J Am Med Inform Assoc       Date:  2011 Sep-Oct       Impact factor: 4.497

7.  Heart disease and stroke statistics--2015 update: a report from the American Heart Association.

Authors:  Dariush Mozaffarian; Emelia J Benjamin; Alan S Go; Donna K Arnett; Michael J Blaha; Mary Cushman; Sarah de Ferranti; Jean-Pierre Després; Heather J Fullerton; Virginia J Howard; Mark D Huffman; Suzanne E Judd; Brett M Kissela; Daniel T Lackland; Judith H Lichtman; Lynda D Lisabeth; Simin Liu; Rachel H Mackey; David B Matchar; Darren K McGuire; Emile R Mohler; Claudia S Moy; Paul Muntner; Michael E Mussolino; Khurram Nasir; Robert W Neumar; Graham Nichol; Latha Palaniappan; Dilip K Pandey; Mathew J Reeves; Carlos J Rodriguez; Paul D Sorlie; Joel Stein; Amytis Towfighi; Tanya N Turan; Salim S Virani; Joshua Z Willey; Daniel Woo; Robert W Yeh; Melanie B Turner
Journal:  Circulation       Date:  2014-12-17       Impact factor: 29.690

8.  Improving performance of natural language processing part-of-speech tagging on clinical narratives through domain adaptation.

Authors:  Jeffrey P Ferraro; Hal Daumé; Scott L Duvall; Wendy W Chapman; Henk Harkema; Peter J Haug
Journal:  J Am Med Inform Assoc       Date:  2013-03-13       Impact factor: 4.497

9.  Identifying patient smoking status from medical discharge records.

Authors:  Ozlem Uzuner; Ira Goldstein; Yuan Luo; Isaac Kohane
Journal:  J Am Med Inform Assoc       Date:  2007-10-18       Impact factor: 4.497

Review 10.  Evaluating temporal relations in clinical text: 2012 i2b2 Challenge.

Authors:  Weiyi Sun; Anna Rumshisky; Ozlem Uzuner
Journal:  J Am Med Inform Assoc       Date:  2013-04-05       Impact factor: 4.497

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  11 in total

Review 1.  Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2.

Authors:  Amber Stubbs; Christopher Kotfila; Hua Xu; Özlem Uzuner
Journal:  J Biomed Inform       Date:  2015-07-22       Impact factor: 6.317

2.  Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus.

Authors:  Amber Stubbs; Özlem Uzuner
Journal:  J Biomed Inform       Date:  2015-08-28       Impact factor: 6.317

Review 3.  De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1.

Authors:  Amber Stubbs; Michele Filannino; Özlem Uzuner
Journal:  J Biomed Inform       Date:  2017-06-11       Impact factor: 6.317

4.  Annotating risk factors for heart disease in clinical narratives for diabetic patients.

Authors:  Amber Stubbs; Özlem Uzuner
Journal:  J Biomed Inform       Date:  2015-05-21       Impact factor: 6.317

5.  Automatic prediction of coronary artery disease from clinical narratives.

Authors:  Kevin Buchan; Michele Filannino; Özlem Uzuner
Journal:  J Biomed Inform       Date:  2017-06-27       Impact factor: 6.317

6.  Practical applications for natural language processing in clinical research: The 2014 i2b2/UTHealth shared tasks.

Authors:  Özlem Uzuner; Amber Stubbs
Journal:  J Biomed Inform       Date:  2015-10-24       Impact factor: 6.317

7.  Toward Understanding Clinical Context of Medication Change Events in Clinical Narratives.

Authors:  Diwakar Mahajan; Jennifer J Liang; Ching-Huei Tsou
Journal:  AMIA Annu Symp Proc       Date:  2022-02-21

8.  Classifying Cyber-Risky Clinical Notes by Employing Natural Language Processing.

Authors:  Suzanna Schmeelk; Martins Samuel Dogo; Yifan Peng; Braja Gopal Patra
Journal:  Proc Annu Hawaii Int Conf Syst Sci       Date:  2022-01-04

Review 9.  Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1.

Authors:  Amber Stubbs; Christopher Kotfila; Özlem Uzuner
Journal:  J Biomed Inform       Date:  2015-07-28       Impact factor: 6.317

10.  Transferability of neural network clinical deidentification systems.

Authors:  Kahyun Lee; Nicholas J Dobbins; Bridget McInnes; Meliha Yetisgen; Özlem Uzuner
Journal:  J Am Med Inform Assoc       Date:  2021-11-25       Impact factor: 7.942

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