%0 Conference Proceedings
%T Clinical Text Mining for Context Sequences Identification
%+ Institute of Information and Communication Technologies (IICT)
%A Boytcheva, Svetla
%Z Part 2: MAKE-Text
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
%C Hamburg, Germany
%Y Andreas Holzinger
%Y Peter Kieseberg
%Y A Min Tjoa
%Y Edgar Weippl
%I Springer International Publishing
%3 Machine Learning and Knowledge Extraction
%V LNCS-11015
%P 223-236
%8 2018-08-27
%D 2018
%R 10.1007/978-3-319-99740-7_15
%K Data mining
%K Text mining
%K Health informatics
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X This paper presents an approach based on sequence mining for identification of context models of diseases described by different medical specialists in clinical text. Clinical narratives contain rich medical terminology, specific abbreviations, and various numerical values. Usually raw clinical texts contain too many typos. Due to the telegraphic style of the text and incomplete sentences, the general part of speech taggers and syntax parsers are not efficient in text processing of non-English clinical text. The proposed approach is language independent. Thus, the method is suitable for processing clinical texts in low resource languages. The experiments are done on pseudonimized outpatient records in Bulgarian language produced by four different specialists for the same cohort of patients suffering from similar disorders. The results show that from the clinical documents can be identified the specialty of the physician. Even the close vocabulary is used in the patient status description there are slight differences in the language used by different physicians. The depth and the details of the description allow to determine different aspects and to identify the focus in the text. The proposed data driven approach will help for automatic clinical text classification depending on the specialty of the physician who wrote the document. The experimental results show high precision and recall in classification task for all classes of specialist represented in the dataset. The comparison of the proposed method with bag of words method show some improvement of the results in document classification task.
%G English
%Z TC 5
%Z TC 8
%Z TC 12
%Z WG 8.4
%Z WG 8.9
%Z WG 12.9
%2 https://inria.hal.science/hal-02060045/document
%2 https://inria.hal.science/hal-02060045/file/472936_1_En_15_Chapter.pdf
%L hal-02060045
%U https://inria.hal.science/hal-02060045
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-TC8
%~ IFIP-WG8-4
%~ IFIP-WG8-9
%~ IFIP-CD-MAKE
%~ IFIP-WG12-9
%~ IFIP-LNCS-11015