## 3 Results and Discussion  
After the execution of the queries, the measures were extracted using the _rec_eval_ tool. It receives the recovered documents and the qrels file as parameters. This tool has been officially developed for its use in many of the tasks organized by the Text REtrieval Conference (TREC). For each query, the values of MAP, R-prec, and P@n were recorded for \(n\in\{5,10,15,100\}\). This procedure was exactly the same for both CQ and EQ. Among the most frequently used measures in information retrieval are MAP, R-prec, and P@n. The Mean Average Precision (MAP) is the mean of the average precision scores for each query:  
\[MAP=\frac{\sum_{q=1}^{Q}Ave(P)}{|Q|} \tag{5}\]  
The average precision (\(Ave(P)\)) emphasizes the assignment of a higher ranking to relevant documents. It is the average of the precision of each of the relevant documents in the ranked sequence:  
\[Ave(P)=\frac{\sum_{k=1}^{n}(P(k)\times rel(k))}{\text{number of relevant documents}} \tag{6}\]  
* \(k\) is the rank in the sequence of retrieved documents;
* \(n\) is the number of retrieved documents;
* \(rel(k)\) is a binary function that assumes the value of 1 if the item at rank \(k\) is a relevant document, and zero if otherwise;
* \(P(k)\) is the precision at cut-off \(k\) on the list. _R-prec_ is the precision after \(R\) documents have been retrieved, where \(R\) is the number of relevant documents for the topic. P@n is the accuracy of the first \(n\) documents recovered. After the evaluation of the results in terms of the average values of the aforementioned measures, there is a clear improvement obtained by the expanded query. As can be seen in Figure 5, there is an increase of approximately 30% in the value of the MAP measure, from 0.261 to 0.335, with the use of query expansion. Regarding the R-prec measure, there is a general improvement of 12%. In relation to P@5 and P@10, the improvement is still significant at about 12% and 13%, which means that even with an increase in the considered number of the first retrieved documents, the system remains robust. In the measure P@15, the improvement was about 12%, while in P@100, it was about 24%. In Table 1, the _MAP_, _R-prec_, _P@5_, _P@10_, _P@15_, and _P@100_ values obtained for each combined and expanded query are recorded. This shows a general improvement in all queries resulting from the expansion of the initial consultation. We can draw a comparison between the obtained results and the outcome of the participants of the TREC2017 Precision Medicine Track. As reported in [27], Table 8 shows the best, median, and worst results per topic from over 133 runs at P@5, P@10, and P@15 for the TREC 2017 Precision Medicine Track using clinical trials. Our method clearly outperforms most of median results in all precision ranges. More specifically, it achieves better precision than the median in 25 out of 29 topics for P@5, 26 out of 29 topics for P@10, and 26 out of 29 topics for P@15. In order to better analyze the results, Figures 6-8 show a comparison of the mean precision of the participants of the TREC2017 Precision Medicine Track and that of the proposed method in the top 5, 10, and 15 retrieved documents. Firstly, Figure 6 shows how our proposed method obtained better or equal results as the mean participants of the TREC2017 Precision Medicine Track, except for the fifth query, which means that none of the first five retrieved documents were relevant due to the high difficulty of the query. Secondly, it can be seen in Figure 7 how the proposed method obtained the same results as the other teams at query number 5. Unlike the precision in five documents, the proposed method always offers better or equal results at P@10. Lastly, Figure 8 proves how the results keep getting better than the mean results of the other teams in most cases, only being unable to reach it at query number 9. This query only has two definitely relevant documents and 60 partially relevant ones, making the retrieval process deeply difficult as the number of first-analyzed documents is increased.