Clinical Practice Guidelines (CPGs) are important means to improve the quality of care by supporting medical staff. Modeling CPGs in a computer-interpretable form is a prerequisite for various computer applications to support their application. However, transforming guidelines in a formal guideline representation is a difficult task. Existing methods and tools demand detailed medical knowledge, knowledge about the formal representations, and a manual modeling. In this chapter we introduce methods and tools for formalizing CPGs and we propose a methodology to reduce the human effort needed in the translation from original textual guidelines to formalized processable knowledge bases. The idea of our methodology is to use Information Extraction methods to help in the semi-automation of guideline content formalization of treatment processes. Thereby, the human modeler will be supported by both automating parts of the modeling process and making the modeling process traceable and comprehensible. Our methodology, called LASSIE, represents a novel method applying a stepwise procedure. The general idea is to use this method to formalize guidelines in any guideline representation language by applying both general steps (i.e., language-independent) and language-specific steps. In order to evaluate both the methodology and the Information Extraction system, a framework was implemented and applied to several guidelines from the medical sub ject of otolaryngology. The framework has been applied to formalize the guidelines in the formal Asbru plan representation. Findings in the evaluation indicate that using semi-automatic, stepwise Information Extraction methods are a valuable instrument to formalize CPGs.
K. Kaiser, S. Miksch: "Modeling Treatment Processes Using Information Extraction"; in: "Advanced Computational Intelligence Paradigms in Healthcare 1", H. Yoshida, A. Jain, A. Ichalkaranje, L. Jain, N. Ichalkaranje (ed.); Springer-Verlag, 2007, ISBN: 978-3-540-47523-1, 189 - 224.
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