Relational learning of pattern-match rules for information extraction
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Introduction to Expert Systems
Introduction to Expert Systems
Text Mining: Predictive Methods for Analyzing Unstructured Information
Text Mining: Predictive Methods for Analyzing Unstructured Information
Design and implementation of the GLIF3 guideline execution engine
Journal of Biomedical Informatics
Introduction to information extraction
AI Communications
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Clinical Practice Guidelines guide decision making in decision problems such as the diagnosis, prevention, etc. for specific clinical circumstances. They are usually available in the form of textual documents written in natural language whose interpretation, however, can make difficult their implementation. Additionally, the high number of available documents and the presence of information for different decision problems in the same document can further hinder their use. In this paper, we propose a framework to extract practices and indications considered to be important in a particular clinical circumstance for a specific decision problem from textual clinical guidelines. The framework operates in two consecutive phases: the first one aims at extracting pieces of information relevant for each decision problem from the documents, while the second one exploits pieces of information in order to generate a structured representation of the clinical practice guidelines for each decision problem. The application to the context of Metabolic Syndrome proves the effectiveness of the proposed framework.