Automated search for patient records: classification of free-text medical reports using conditional random fields

Authors:
Ivko Cvejic;Jun Zhang;James Marx;Judy Tjoe
Affiliations:
University of Wisconsin-Milwaukee, Milwaukee, WI, USA;University of Wisconsin-Milwaukee, Milwaukee, WI, USA;Aurora Health Care, Milwaukee, WI, USA;Aurora Health Care, Milwaukee, WI, USA
Venue:
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Year:
2012

Citing 5
Cited 0

Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Accurate unlexicalized parsing

ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Assessing clinical trial eligibility with logic expression queries

Data & Knowledge Engineering
Introduction to Information Retrieval

Introduction to Information Retrieval
Automatic patient search for breast cancer clinical trials using free-text medical reports

Proceedings of the 1st ACM International Health Informatics Symposium

Quantified Score

Hi-index	0.00

Visualization

Abstract

The purpose of this research is to develop a classifier to automatically identify qualified patients for breast cancer clinical trials from free-text medical reports. Specifically, we trained a conditional random field based classifier to identify patterns in sentences from free-text reports that are consistent with trial criteria. Experimental results indicate that this method works well, outperforming several competing techniques. Our work is important in two ways. First, it could be used to increase efficiency and decrease the cost of the patient enrollment process for clinical trials. Second, it could be extended and adapted to other patient identification problems.