Mayo Clinic NLP System for Patient Smoking Status Identification

Guergana K. Savova, Philip V. Ogren, Patrick H. Duffy, James D. Buntrock, Christopher G. Chute

Research output: Contribution to journalArticlepeer-review

Abstract

This article describes our system entry for the 2006 I2B2 contest "Challenges in Natural Language Processing for Clinical Data" for the task of identifying the smoking status of patients. Our system makes the simplifying assumption that patient-level smoking status determination can be achieved by accurately classifying individual sentences from a patient's record. We created our system with reusable text analysis components built on the Unstructured Information Management Architecture and Weka. This reuse of code minimized the development effort related specifically to our smoking status classifier. We report precision, recall, F-score, and 95% exact confidence intervals for each metric. Recasting the classification task for the sentence level and reusing code from other text analysis projects allowed us to quickly build a classification system that performs with a system F-score of 92.64 based on held-out data tests and of 85.57 on the formal evaluation data. Our general medical natural language engine is easily adaptable to a real-world medical informatics application. Some of the limitations as applied to the use-case are negation detection and temporal resolution.

Original languageEnglish (US)
Pages (from-to)25-28
Number of pages4
JournalJournal of the American Medical Informatics Association
Volume15
Issue number1
DOIs
StatePublished - Jan 2008
Externally publishedYes

ASJC Scopus subject areas

  • Health Informatics

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