Using Regular Expressions to Abstract Blood Pressure and Treatment Intensification Information from the Text of Physician Notes | Zendy

Alexander Turchin | Zendy; Nikheel S. Kolatkar | Zendy; Richard W. Grant | Zendy; Eric C. Makhni | Zendy; M. Pendergrass | Zendy; Jonathan S. Einbinder | Zendy

Open Access

Using Regular Expressions to Abstract Blood Pressure and Treatment Intensification Information from the Text of Physician Notes

Author(s) -

Alexander Turchin,

Nikheel S. Kolatkar,

Richard W. Grant,

Eric C. Makhni,

M. Pendergrass,

Jonathan S. Einbinder

Publication year - 2006

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1197/jamia.m2078

Subject(s) - blood pressure , gold standard (test) , medicine , sensitivity (control systems) , abstraction , software , disease , text messaging , identification (biology) , computer science , data mining , intensive care medicine , world wide web , programming language , botany , epistemology , electronic engineering , engineering , biology , philosophy

This case study examined the utility of regular expressions to identify clinical data relevant to the epidemiology of treatment of hypertension. We designed a software tool that employed regular expressions to identify and extract instances of documented blood pressure values and anti-hypertensive treatment intensification from the text of physician notes. We determined sensitivity, specificity and precision of identification of blood pressure values and anti-hypertensive treatment intensification using a gold standard of manual abstraction of 600 notes by two independent reviewers. The software processed 370 Mb of text per hour, and identified elevated blood pressure documented in free text physician notes with sensitivity and specificity of 98%, and precision of 93.2%. Anti-hypertensive treatment intensification was identified with sensitivity 83.8%, specificity of 95.0%, and precision of 85.9%. Regular expressions can be an effective method for focused information extraction tasks related to high-priority disease areas such as hypertension.

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