Rule-based Information Extraction from Patients' Clinical Data
KUPŚĆ, Anna
Equipe de Recherche en Syntaxe et Sémantique [ERSS]
Instytut Podstaw Informatyki [IPI PAN]
Linguistic signs, grammar and meaning: computational logic for natural language [SIGNES]
Equipe de Recherche en Syntaxe et Sémantique [ERSS]
Instytut Podstaw Informatyki [IPI PAN]
Linguistic signs, grammar and meaning: computational logic for natural language [SIGNES]
KUPŚĆ, Anna
Equipe de Recherche en Syntaxe et Sémantique [ERSS]
Instytut Podstaw Informatyki [IPI PAN]
Linguistic signs, grammar and meaning: computational logic for natural language [SIGNES]
< Réduire
Equipe de Recherche en Syntaxe et Sémantique [ERSS]
Instytut Podstaw Informatyki [IPI PAN]
Linguistic signs, grammar and meaning: computational logic for natural language [SIGNES]
Langue
en
Article de revue
Ce document a été publié dans
Journal of Biomedical Informatics. 2009, vol. 42, n° 5, p. 923-936
Elsevier
Résumé en anglais
The paper describes a rule-based information extraction (IE) system developed for Polish medical texts. We present two applications designed to select data from medical documentation in Polish: mammography reports and ...Lire la suite >
The paper describes a rule-based information extraction (IE) system developed for Polish medical texts. We present two applications designed to select data from medical documentation in Polish: mammography reports and hospital records of diabetic patients. First, we have designed a special ontology that subsequently had its concepts translated into two separate models, represented as typed feature structure (TFS) hierarchies, complying with the format required by the IE platform we adopted. Then, we used dedicated IE grammars to process documents and fill in templates provided by the models. In particular, in the grammars, we addressed such linguistic issues as: ambiguous keywords, negation, coordination or anaphoric expressions. Resolving some of these problems has been deferred to a post-processing phase where the extracted information is further grouped and structured into more complex templates. To this end, we defined special heuristic algorithms on the basis of sample data. The evaluation of the implemented procedures shows their usability for clinical data extraction tasks. For most of the evaluated templates, precision and recall well above 80% were obtained.< Réduire
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