Identifying similar patients might greatly facilitate the treatment of a given patient, enabling to observe the response and outcome to a particular treatment. Case-based retrieval services dealing with natural language processing are of major importance to deal with the significant amount of unstructured clinical data. In this paper, we present the development and evaluation of a case-based retrieval (CBR) service tested on a collection of Italian pediatric cardiology cases. Cases are indexed and a search engine is proposed. Search functionalities, such as interactive MeSH normalization and relevance feedback, are proposed. While the qualitative evaluation aims to provide feedback and recommendations, the quantitative evaluation enables to estimate the precision of the system. In more than half of the cases and for up to two thirds of them, the system is able to suggest a similar episode of care at first rank. With an improvement of the feedback relevance strategy, we can expect an improvement of the precision. The CBR can be expanded to multilingual EHR and other fields.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 firstname.lastname@example.org
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 email@example.com