Evaluation of a Machine Learning Method to Rank PubMed Central Articles For Clinical Relevancy: NCH at TREC 2016 Clinical Decision Support Track
The goal of the TREC 2016 Clinical Decision Support track is to retrieve and rank PubMed Central (PMC) articles that are relevant to potential tests, treatments or diagnoses of a patient case narrative. Our objective was to develop a machine learning method to rank PMC articles by taking advantage of the previous years' gold standard TREC competition results. The classifier we trained on 2014 data achieved high accuracy when tested with 2015 data (P10=0.59 and infNDCG=0.67) compared with the Elasticsearch method (P10=0.19 and infNDCG=0.22) However, when we applied the same classifier approach with both the 2014 and 2015 data sets combined, and then tested this method against the 2016 cases, the results did not improve over the Elasticsearch method. We concluded that although the machine learning approach was found effective on predicting previous years' results, it was not as effective for 2016 data, most likely due to the change in the topic structures.
TREC