Using Regular Expressions to Abstract Blood Pressure and Treatment Intensification Information from the Text of Physician Notes
Open Access
- 1 November 2006
- journal article
- research article
- Published by Oxford University Press (OUP) in Journal of the American Medical Informatics Association
- Vol. 13 (6), 691-695
- https://doi.org/10.1197/jamia.m2078
Abstract
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.This publication has 21 references indexed in Scilit:
- Natural language processing to extract medical problems from electronic clinical documents: Performance evaluationJournal of Biomedical Informatics, 2006
- Therapeutic Inertia Is an Impediment to Achieving the Healthy People 2010 Blood Pressure Control GoalsHypertension, 2006
- Natural Language Processing in the Electronic Medical Record: Assessing Clinician Adherence to Tobacco Treatment GuidelinesAmerican Journal of Preventive Medicine, 2005
- The use of routinely collected computer data for research in primary care: opportunities and challengesFamily Practice, 2005
- Automated Detection of Adverse Events Using Natural Language Processing of Discharge SummariesJournal of the American Medical Informatics Association, 2005
- A Randomized Trial of Electronic Clinical Reminders to Improve Quality of Care for Diabetes and Coronary Artery DiseaseJournal of the American Medical Informatics Association, 2005
- MediClass: A System for Detecting and Classifying Encounter-based Clinical Events in Any Electronic Medical RecordJournal of the American Medical Informatics Association, 2005
- Electronic Screening of Dictated Reports to Identify Patients with Do-Not-Resuscitate StatusJournal of the American Medical Informatics Association, 2004
- Use of General-purpose Negation Detection to Augment Concept Indexing of Medical Documents: A Quantitative Study Using the UMLSJournal of the American Medical Informatics Association, 2001
- The sixth report of the Joint National Committee on prevention, detection, evaluation, and treatment of high blood pressureArchives of Internal Medicine, 1997