Objective. Formalizing clinical practice guidelines for a subsequent computer-supported processing is a challenging, but burdensome and time-consuming task. Existing methods and tools to support this task demand detailed medical knowledge, knowledge about the formal representations, and a manual modeling. Furthermore, formalized guideline documents mostly fall far short in terms of readability and understandability for the human domain modeler. We propose a new multi-step approach using information extraction methods to support the human modeler by both automating parts of the modeling process and making the modeling process traceable and comprehensible. This paper addresses the first steps to obtain a representation containing processes which is independent of the final guideline representation language. We have developed and evaluated several heuristics without the need to apply Natural Language Understanding and implemented them in a framework to apply them to several guidelines from the medical subject of otolaryngology. Findings in the evaluation indicate that using semi-automatic, step-wise information extraction methods are a valuable Preprint submitted to Elsevier Science instrument to formalize CPGs. Our evaluation shows that a heuristic-based approach can achieve good results, especially for guidelines with a major portion of semi-structured text. It can be applied to guidelines irrespective to the final guideline representation format.
In REF a heuristic-based approach using information extraction methods independent of the final guideline representation language was defined.
How can Information Extraction ease formalizing treatment processes in clinical practice guidelines ? A method and its evaluation ⋆
Artificial intelligence in medicine
Artificial intelligence in medicine
Medicine Computer Science