@inproceedings{Hu2015,
abstract = {To ensure timely use of new results from medical research in daily medical practice, evidence-based medical guidelines must be up-dated using the latest medical articles as evidences. Finding such new relevant medical evidence manually is time consuming and labor inten-sive. 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 pro-pose the use of a semantic distance measure to automatically find rele-vant 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 gather-ing a large corpus for analysis. We have conducted several experiments that use our semantic distance measure to find new relevant evidence for guideline updates. We selected two versions of the Dutch Breast Cancer Guidelines (2004 and 2012), and we checked if the new evidence items in the 2012 version could be found by using our method. The experiment shows that our method can not only find at least some evidence for 10 out of the 16 guideline statements in our experiment (i.e. a reasonable recall), but it also returns reasonably small numbers of evidence candi-dates (i.e. a good precision) with an acceptable real-time performance (an average of approximately 10 minutes for each guideline statement).},
author = {Hu, Qing and Huang, Zhisheng and {Ten Teije}, Annette and {Van Harmelen}, Frank},
booktitle = {Artificial Intelligence in Medicine, 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015, Proceedings AIME},
file = {:Users/annette/Dropbox/AnnetteDropBoxVU/research/conferences/2015AIME/papers/final/2015AIMEguidelineSDQingHuFinal.pdf:pdf},
pages = {307--316},
publisher = {Springer},
title = {{Detecting New Evidence for Evidence-based Guidelines Using a Semantic Distance Method}},
url = {http://www.cs.vu.nl/{~}frankh/postscript/AIME15-GuidelineUpdate.pdf http://www.cs.vu.nl/{~}annette/papers-pdf/2015AIMEguidelineSDQingHu.pdf},
year = {2015}
}

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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 pro-pose the use of a semantic distance measure to automatically find rele-vant 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 gather-ing a large corpus for analysis. We have conducted several experiments that use our semantic distance measure to find new relevant evidence for guideline updates. We selected two versions of the Dutch Breast Cancer Guidelines (2004 and 2012), and we checked if the new evidence items in the 2012 version could be found by using our method. 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