HTNSystem: Hypertension information extraction system for unstructured clinical notes

Jitendra Jonnagaddala; Siaw Teng Liaw; Pradeep Ray; Manish Kumar; Hong Jie Dai

doi:10.1007/978-3-319-13987-6_21

HTNSystem: Hypertension information extraction system for unstructured clinical notes

Jitendra Jonnagaddala, Siaw Teng Liaw, Pradeep Ray, Manish Kumar, Hong Jie Dai

Graduate Institute of Biomedical Informatics

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

Abstract

Hypertension (HTN) relevant information has great application potential in cohort discovery and building predictive models for prevention and surveillance. Unfortunately most of this valuable patient information is buried in the form of unstructured clinical notes. In this study we present HTN information extraction system called HTNSystem which is capable of extracting mentions of HTN and inferring HTN from BP lab values. HTNSystem is a rule based system which implements MetaMap as a core component together with custom built BP value extractor and post processing components. It is evaluated on a corpus of 514 clinical notes (82.92% F-measure). HTNSystem is distributed as an open source command line tool available at https://github.com/TCRNBioinformatics/HTNSystem.

Original language	English
Pages (from-to)	219-227
Number of pages	9
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8916
DOIs	https://doi.org/10.1007/978-3-319-13987-6_21
Publication status	Published - 2014

Keywords

Apache Ruta
Apache UIMA
Blood pressure
Hypertension
Information extraction
Rule based
Text mining

ASJC Scopus subject areas

Computer Science(all)
Theoretical Computer Science

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10.1007/978-3-319-13987-6_21

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HTNSystem : Hypertension information extraction system for unstructured clinical notes. / Jonnagaddala, Jitendra; Liaw, Siaw Teng; Ray, Pradeep; Kumar, Manish; Dai, Hong Jie.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 8916, 2014, p. 219-227.

Research output: Contribution to journal › Article › peer-review

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HTNSystem: Hypertension information extraction system for unstructured clinical notes

Abstract

Keywords

ASJC Scopus subject areas

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