Change search
ReferencesLink to record
Permanent link

Direct link
Clinical text retrieval: an overview of basic building blocks and applications
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2014 (English)In: Professional search in the modern world: COST action IC1002 on multilingual and multifaceted interactive information access / [ed] Georgios Paltoglou, Fernando Loizides, Preben Hansen, Springer , 2014, 147-165 p.Chapter in book (Other academic)
Abstract [en]

This article describes information retrieval, natural language processing and text mining of electronic patient record text, also called clinical text. Clinical text is written by physicians and nurses to docu- ment the health care process of the patient. First we describe some char- acteristics of clinical text, followed by the automatic preprocessing of the text that is necessary for making it usable for some applications. We also describe some applications for clinicians including spelling and grammar checking, ICD-10 diagnosis code assignment, as well as other applications for hospital management such as ICD-10 diagnosis code validation and detection of adverse events such as hospital acquired infections. Part of the preprocessing makes the clinical text useful for faceted search, al- though clinical text already has some keys for performing faceted search such as gender, age, ICD-10 diagnosis codes, ATC drug codes, etc. Pre- processing makes use of ICD-10 codes and the SNOMED-CT textual descriptions. ICD-10 codes and SNOMED-CT are available in several languages and can be considered the modern Greek or Latin of medical language. The basic research presented here has its roots in the chal- lenges described by the health care sector. These challenges have been partially solved in academia, and we believe the solutions will be adapted to the health care sector in real world applications.

Place, publisher, year, edition, pages
Springer , 2014. 147-165 p.
, Lecture Notes in Computer Science, Vol 8830
Keyword [en]
Information retrieval, electronic patient records, clinical text, spell checking, ICD-10, SNOMED-CT, Swedish
National Category
Information Systems
Research subject
Computer and Systems Sciences
URN: urn:nbn:se:su:diva-108676DOI: 10.1007/978-3-319-12511-4ISBN: 978-3-319-12510-7OAI: diva2:760054
Available from: 2014-11-03 Created: 2014-11-03 Last updated: 2014-11-04Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Dalianis, Hercules
By organisation
Department of Computer and Systems Sciences
Information Systems

Search outside of DiVA

GoogleGoogle Scholar
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 38 hits
ReferencesLink to record
Permanent link

Direct link