Automatic patient search for breast cancer clinical trials using free-text medical reports

Authors:
Jun Zhang;Yingying Gu;Weisong Liu;Wen Hu;Tian Zhao;Xiangming Mu;James Marx;Floyd Frost;Judy Tjoe
Affiliations:
University of Wisconsin-Milwaukee, Milwaukee, WI, USA;University of Wisconsin-Milwaukee, Milwaukee, WI, USA;University of Wisconsin-Milwaukee, Milwaukee, WI, USA;University of Wisconsin-Milwaukee, Milwaukee, WI, USA;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;Aurora Health Care, Milwaukee, WI, USA
Venue:
Proceedings of the 1st ACM International Health Informatics Symposium
Year:
2010

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Abstract

The purpose of this work is to develop algorithms to automatically identify qualified patients for breast cancer clinical trials from free-text medical reports. Specifically, we developed an algorithm, called subtree match, that achieves this by finding structural patterns in free-text patient report sentences that are consistent with given trial criteria. Experimental results indicate that this technique is effective and performs better than several competing techniques. Our work is useful in two respects. First, it can potentially increase the efficiency and reduce the cost of the patient enrollment process. Second, it can be extended/adapted to the clinical trials of other diseases