Abstract
Evidence-based medical guidelines contain a collection of recommendations which have been created using the best clinical research findings (a.k.a. evidences) of the highest value to aid in the delivery of optimum clinical care to patients. In evidence-based medical guidelines, the conclusions (a.k.a. recommendations) are marked with different evidence levels according to quality of the supporting evidences. Finding new relevant and higher quality evidences is an important issue for supporting the process of updating medical guidelines. In this paper, we propose a method to automatically identify all evidence classes. Furthermore, the proposed approach has been implemented by a rule-based approach, in which the identification knowledge is formalized as a set of rules in the declarative logic programming language Prolog, so that the knowledge can be easily maintained, updated, and re-used. Our experiments show that the proposed method for identifying the evidence quality has a recall of 0.35 and a precision of 0.42. For the identification of A-class evidences (the top evidence class), the performance of the proposed method improves to recall = 0.63 and precision = 0.74.
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Acknowledgements
This work is partially supported by the European Commission under the 7th framework programme EURECA Project (FP7-ICT-2011-7, Grant 288048).
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Huang, Z., Hu, Q., ten Teije, A., van Harmelen, F. (2015). Identifying Evidence Quality for Updating Evidence-Based Medical Guidelines. In: Riaño, D., Lenz, R., Miksch, S., Peleg, M., Reichert, M., ten Teije, A. (eds) Knowledge Representation for Health Care. AIME 2015. Lecture Notes in Computer Science(), vol 9485. Springer, Cham. https://doi.org/10.1007/978-3-319-26585-8_4
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DOI: https://doi.org/10.1007/978-3-319-26585-8_4
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