Clinical Text Mining for Context Sequences Identification

Abstract : 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.
Complete list of metadatas

Cited literature [20 references]  Display  Hide  Download

https://hal.inria.fr/hal-02060045
Contributor : Hal Ifip <>
Submitted on : Thursday, March 7, 2019 - 10:36:43 AM
Last modification on : Friday, September 6, 2019 - 11:14:03 AM
Long-term archiving on: Saturday, June 8, 2019 - 1:30:52 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2021-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Svetla Boytcheva. Clinical Text Mining for Context Sequences Identification. 2nd International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE), Aug 2018, Hamburg, Germany. pp.223-236, ⟨10.1007/978-3-319-99740-7_15⟩. ⟨hal-02060045⟩

Share

Metrics

Record views

43