Literature DB >> 30815119

CDA-Compliant Section Annotation of German-Language Discharge Summaries: Guideline Development, Annotation Campaign, Section Classification.

Christina Lohr1, Stephanie Luther1, Franz Matthies1, Luise Modersohn1, Danny Ammon2, Kutaiba Saleh2, Andreas G Henkel2, Michael Kiehntopf3, Udo Hahn1.   

Abstract

We present the outcome of an annotation effort targeting the content-sensitive segmentation of German clinical reports into sections. We recruited an annotation team of up to eight medical students to annotate a clinical text corpus on a sentence-by-sentence basis in four pre-annotation iterations and one final main annotation step. The annotation scheme we came up with adheres to categories developed for clinical documents in the HL7-CDA (Clinical Document Architecture) standard for section headings. Once the scheme became stable, we ran the main annotation campaign on the complete set of roughly 1,000 clinical documents. Due to its reliance on the CDA standard, the annotation scheme allows the integration of legacy and newly produced clinical documents within a common pipeline. We then made direct use of the annotations by training a baseline classifier to automatically identify sections in clinical reports.

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Mesh:

Year:  2018        PMID: 30815119      PMCID: PMC6371337     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  4 in total

1.  Development and evaluation of a clinical note section header terminology.

Authors:  Joshua C Denny; Randolph A Miller; Kevin B Johnson; Anderson Spickard
Journal:  AMIA Annu Symp Proc       Date:  2008-11-06

2.  Evaluation of a method to identify and categorize section headers in clinical documents.

Authors:  Joshua C Denny; Anderson Spickard; Kevin B Johnson; Neeraja B Peterson; Josh F Peterson; Randolph A Miller
Journal:  J Am Med Inform Assoc       Date:  2009-08-28       Impact factor: 4.497

3.  Developing a section labeler for clinical documents.

Authors:  Peter J Haug; Xinzi Wu; Jeffery P Ferraro; Guergana K Savova; Stanley M Huff; Christopher G Chute
Journal:  AMIA Annu Symp Proc       Date:  2014-11-14

4.  Scaling Out and Evaluation of OBSecAn, an Automated Section Annotator for Semi-Structured Clinical Documents, on a Large VA Clinical Corpus.

Authors:  Le-Thuy T Tran; Guy Divita; Andrew Redd; Marjorie E Carter; Matthew Samore; Adi V Gundlapalli
Journal:  AMIA Annu Symp Proc       Date:  2015-11-05
  4 in total
  4 in total

1.  Current approaches to identify sections within clinical narratives from electronic health records: a systematic review.

Authors:  Alexandra Pomares-Quimbaya; Markus Kreuzthaler; Stefan Schulz
Journal:  BMC Med Res Methodol       Date:  2019-07-18       Impact factor: 4.615

2.  Annotation and initial evaluation of a large annotated German oncological corpus.

Authors:  Madeleine Kittner; Mario Lamping; Damian T Rieke; Julian Götze; Bariya Bajwa; Ivan Jelas; Gina Rüter; Hanjo Hautow; Mario Sänger; Maryam Habibi; Marit Zettwitz; Till de Bortoli; Leonie Ostermann; Jurica Ševa; Johannes Starlinger; Oliver Kohlbacher; Nisar P Malek; Ulrich Keilholz; Ulf Leser
Journal:  JAMIA Open       Date:  2021-04-19

3.  Automatic extraction of 12 cardiovascular concepts from German discharge letters using pre-trained language models.

Authors:  Phillip Richter-Pechanski; Nicolas A Geis; Christina Kiriakou; Dominic M Schwab; Christoph Dieterich
Journal:  Digit Health       Date:  2021-11-26

4.  Optimized Identification of Advanced Chronic Kidney Disease and Absence of Kidney Disease by Combining Different Electronic Health Data Resources and by Applying Machine Learning Strategies.

Authors:  Christoph Weber; Lena Röschke; Luise Modersohn; Christina Lohr; Tobias Kolditz; Udo Hahn; Danny Ammon; Boris Betz; Michael Kiehntopf
Journal:  J Clin Med       Date:  2020-09-12       Impact factor: 4.241

  4 in total

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