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 |
| Publication status | Published - 2014 |
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Keywords
- Apache Ruta
- Apache UIMA
- Blood pressure
- Hypertension
- Information extraction
- Rule based
- Text mining
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
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
}
TY - JOUR
T1 - HTNSystem
T2 - Hypertension information extraction system for unstructured clinical notes
AU - Jonnagaddala, Jitendra
AU - Liaw, Siaw Teng
AU - Ray, Pradeep
AU - Kumar, Manish
AU - Dai, Hong Jie
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Apache Ruta
KW - Apache UIMA
KW - Blood pressure
KW - Hypertension
KW - Information extraction
KW - Rule based
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=84911925951&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84911925951&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:84911925951
VL - 8916
SP - 219
EP - 227
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
SN - 0302-9743
ER -