%0 Journal Article
%T Context
%A Maqbool Hussain
%A Muhammad Afzal
%A Robert Brian Haynes
%A Sungyoung Lee
%J Health Informatics Journal
%@ 1741-2811
%D 2019
%R 10.1177/1460458217719560
%X Processing huge repository of medical literature for extracting relevant and high-quality evidences demands efficient evidence support methods. We aim at developing methods to automate the process of finding quality evidences from a plethora of literature documents and grade them according to the context (local condition). We propose a two-level methodology for quality recognition and grading of evidences. First, quality is recognized using quality recognition model; second, context-aware grading of evidences is accomplished. Using 10-fold cross-validation, the proposed quality recognition model achieved an accuracy of 92.14£¿percent and improved the baseline system accuracy by about 24£¿percent. The proposed context-aware grading method graded 808 out of 1354 test evidences as highly beneficial for treatment purpose. This infers that around 60£¿percent evidences shall be given more importance as compared to the other 40£¿percent evidences. The inclusion of context in recommendation of evidence makes the process of evidence-based decision-making ¡°situation-aware.
%K context-aware evidence grading
%K evidence-based medicine
%K evidence-based practice
%K evidence informed decision
%K quality recognition
%U https://journals.sagepub.com/doi/full/10.1177/1460458217719560