Table 3, Table 4, and Table 5 list quantitative outputs for SemRep, Combo-enhanced dynamic summarization, and summarization using the conventional treatment schema.
Combo algorithm-enhanced dynamic summarization outperformed conventional summarization and the baseline method in recall, but was outperformed by conventional summarization in terms of precision.
For the prevention data, Semantic MEDLINE enhanced with dynamic summarization achieved average recall and precision scores of 0.655 and 0.329.
For the drug treatment data, Semantic MEDLINE enhanced with dynamic summarization achieved average recall and precision scores of 0.848 and 0.377, while conventional summarization produced 0.583 average recall and 0.712 average precision, and the baseline method yielded average recall and precision values of 0.252 and 0.277.
The PubMed search queries retrieved varying quantities of output, as did SemRep, conventional, and dynamic summarization.
However, in analyses for both points-of-view, dynamic summarization performed better than the other two processes.
Because Semantic MEDLINE is a pipeline application, data loss can be tracked by documenting the first sequential process (among PubMed retrieval, SemRep, and dynamic summarization) that does not include a reference standard intervention.
In this case, PubMed retrieval did not include 41.2% (7 false negatives) while SemRep output did not include 35.3% (6 false negatives) and dynamic summarization output did not include 23.5% (4 false negatives).
In tracking the 23 false negatives that addressed a drug treatment point-of-view, PubMed retrieval did not garner 43.5% (10 false negatives); SemRep output did not include 47.8% (11 false negatives); and dynamic summarization did not identify 8.7% (2 false negatives).
Both dynamic and conventional summarization regularly outperformed the baseline method.
