A Textual Representation Scheme for Identifying Clinical Relationships in Patient Records

• Published in: 2010 International Conference on Machine Learning and Applications Vol. 2010; pp. 995 - 998
• Main Authors: Doan, Rezarta Islamaj, Neveol, Aurelie, Lu, Zhiyong
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 04.02.2011
• Subjects: Bioinformatics; clinical text; Context modeling; Diseases; Feature extraction; Machine learning; medical concepts; patient records; relationship identification; relationship representation; Semantics; Support vector machines
• ISBN: 1424492114; 9781424492114
• DOI: 10.1109/ICMLA.2010.164

The identification of relationships between clinical concepts in patient records is a preliminary step for many important applications in medical informatics, ranging from quality of care to hypothesis generation. In this work we describe an approach that facilitates the automatic recognition of relationships defined between two different concepts in text. Unlike the traditional bag-of-words representation, in this work, a relationship is represented with a scheme of five distinct context-blocks based on the position of concepts in the text. This scheme was applied to eight different relationships, between medical problems, treatments and tests, on a set of 349 patient records from the 4th i2b2 challenge. Results show that the context-block representation was very successful (F-Measure = 0.775) compared to the bag-of-words model (F-Measure = 0.402). The advantage of this representation scheme was the correct management of word position information, which may be critical in identifying certain relationships.