An Evolutionary Approach to the Annotation of Discharge Summaries.

Annotation guideline Clinical text corpus German discharge summaries

Journal

Studies in health technology and informatics
ISSN: 1879-8365
Titre abrégé: Stud Health Technol Inform
Pays: Netherlands
ID NLM: 9214582

Informations de publication

Date de publication:
16 Jun 2020
Historique:
entrez: 24 6 2020
pubmed: 24 6 2020
medline: 15 8 2020
Statut: ppublish

Résumé

We here describe the evolution of annotation guidelines for major clinical named entities, namely Diagnosis, Findings and Symptoms, on a corpus of approximately 1,000 German discharge letters. Due to their intrinsic opaqueness and complexity, clinical annotation tasks require continuous guideline tuning, beginning from the initial definition of crucial entities and the subsequent iterative evolution of guidelines based on empirical evidence. We describe rationales for adaptation, with focus on several metrical criteria and task-centered clinical constraints.

Identifiants

pubmed: 32570340
pii: SHTI200116
doi: 10.3233/SHTI200116
doi:

Types de publication

Journal Article

Langues

eng

Pagination

28-32

Auteurs

Christina Lohr (C)

Jena University Language & Information Engineering (JULIE) Lab Friedrich-Schiller-Universität Jena, Germany.
SMITH Consortium of the German Medical Informatics Initiative.

Luise Modersohn (L)

Jena University Language & Information Engineering (JULIE) Lab Friedrich-Schiller-Universität Jena, Germany.
SMITH Consortium of the German Medical Informatics Initiative.

Johannes Hellrich (J)

Jena University Language & Information Engineering (JULIE) Lab Friedrich-Schiller-Universität Jena, Germany.
SMITH Consortium of the German Medical Informatics Initiative.

Tobias Kolditz (T)

Jena University Language & Information Engineering (JULIE) Lab Friedrich-Schiller-Universität Jena, Germany.
SMITH Consortium of the German Medical Informatics Initiative.

Udo Hahn (U)

Jena University Language & Information Engineering (JULIE) Lab Friedrich-Schiller-Universität Jena, Germany.
SMITH Consortium of the German Medical Informatics Initiative.

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Classifications MeSH