Detecting New Evidences for Evidence-Based Medical Guidelines with Journal Filtering
Evidence-based medical guidelines are systematically developed recommendations with the aim to assist practitioner and patients decisions regarding appropriate health care for specific clinical circumstances, and are based on evidence described in medical research papers. Evidence-based medical guidelines should be regularly updated, such that they can serve medical practice using based on the latest medical research evidence. A usual approach to detecting new evidences is to use a set of terms which appear in a guideline conclusion or recommendation and create queries over a bio-medical search engine such as PubMed with a ranking over a selected subset of terms to search for relevant new research papers. However, the sizes of the found relevant papers are usually very large (i.e. over a few hundreds, even thousands), which results in a low precision of the search. This makes it for medical professionals quite difficult to find which papers are really interesting and useful for updating the guideline. We propose a filtering step to decrease the number of papers. More exactly we are interested in the question if we can reduce the number of papers with no or a slightly lower recall. A plausible approach is to introduce journal filtering, such that evidence appear in those top journals are preferred.
In this paper, we extend our approach of detecting new papers for updating evidence-based medical guideline with a journal filtering step. We report our experiments and show that (1) the method with journal filtering can indeed gain a large reduction of the number of papers (69.73%) with a slightly lower recall (14.29%); (2) we show that the journal filtering method keeps relatively more high level evidence papers (category A) and removes all the low level evidence papers (category D).
KeywordsRelevant Paper Guideline Statement Lower Recall Provenance Information General Medical Journal
This work is partially supported by the European Commission under the 7th framework programme EURECA Project, the Dutch national project COMMIT/Data2Semantics, the major international cooperation project No. 61420106005 funded by China National Foundation of Natural Science. The first author is funded by the China Scholarship Council.
- 1.Ait-Mokhtar, S., Bruijn, B.D., Hagege, C., Rupi, P.: Initial prototype for relation identification between concepts, D3.2. Technical report, EURECA Project (2013)Google Scholar
- 2.Aït-Mokhtar, S., Chanod, J.-P., Roux, C.: Robustness beyond shallowness: incremental deep parsing. Natural Lang. Eng. 8(2), 121–144 (2002)Google Scholar
- 3.Fensel, D., van Harmelen, F., Andersson, B., Brennan, P., Cunningham, H., Della Valle, E., Fischer, F., Huang, Z., Kiryakov, A., Lee, T., School, L., Tresp, V., Wesner, S., Witbrock, M., Zhong, N., LarKC, T.: A platform for web-scale reasoning. In: Proceedings of the IEEE International Conference on Semantic Computing (ICSC 2008). IEEE Computer Society Press, CA (2008)Google Scholar
- 4.Hu, Q., Huang, Z., den Teije, A., van Harmelen, F.: Detecting new evidence for evidence-based guidelines using a semantic distance method. In: Proceedings of the 15th Conference on Artificial Intelligence in Medicine (AIME 2015) (2015)Google Scholar
- 5.Hu, Q., Huang, Z., ten Teije, A., van Harmelen, F., Marshall, M., Dekker, A.: A topic-centric approach to detecting new evidences for evidence-based medical guidelines. In: Proceedings of HEALTHINF 2016, Rome (2016)Google Scholar
- 6.Huang, Z., Hu, Q., ten Teije, A., van Harmelen, F.: Identifying evidence quality for updating evidence-based medical guidelines. In: Proceedings of International Joint Workshop KR4HC 2015 - ProHealth 2015 (2015)Google Scholar
- 7.Huang, Z., ten Teije, A., van Harmelen, F., Ait-Mokhtar, S.: Semantic representation of evidence-based clinical guidelines. In: Proceedings of 6th International Workshop on Knowledge Representation for Health Care (KR4HC 2014) (2014)Google Scholar
- 9.Lewiaon, G., Sullivan, R.: The impact of cancer research: how publications influence uk cancer clinical guidelines. Bristish J. Cancer (2008)Google Scholar
- 10.NABON. Breast cancer, dutch guideline, version 2.0. Technical report, Integraal kankercentrum Netherland, Nationaal Borstkanker Overleg Nederland (2012)Google Scholar
- 11.NSRS. Guideline complex regional pain syndrome type i. Technical report, Netherlands Society of Rehabilitation Specialists (2006)Google Scholar
- 12.Reinders, R., ten Teije, A., Huang, Z.: Finding evidence for updates in medical guideline. In: Proceedings of HEALTHINF 2015, Lisbon (2015)Google Scholar