Detecting New Evidence for Evidence-Based Guidelines Using a Semantic Distance Method

To ensure timely use of new results from medical research in daily medical practice, evidence-based medical guidelines must be updated using the latest medical articles as evidences. Finding such new relevant medical evidence manually is time consuming and labor intensive. Traditional information retrieval methods can improve the efficiency of finding evidence from the medical literature, but they usually require a large training corpus for determining relevance. This means that both the manual approach and traditional IR approaches are not suitable for automatically finding new medical evidence in realtime. This paper propose the use of a semantic distance measure to automatically find relevant new evidence to support guideline updates. The advantage of using our semantic distance measure is that this relevance measure can be easily obtained from a search engine (e.g., PubMed), rather then gathering a large corpus for analysis. We have conducted several experiments that use our sem...

• Type: Journal
• Year: 2015
• Where: AIME
• Authors: Qing Hu, Zhisheng Huang, Annette ten Teije, Frank van Harmelen
• Keywords: AIME 2015, Artificial Intelligence