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. 2015 Nov 5:2015:2015-24.
eCollection 2015.

Classification of Clinically Useful Sentences in MEDLINE

Mohammad Amin Morid  1 , Siddhartha Jonnalagadda  2 , Marcelo Fiszman  3 , Kalpana Raja  2 , Guilherme Del Fiol  4
Affiliations

Classification of Clinically Useful Sentences in MEDLINE

Mohammad Amin Morid et al. AMIA Annu Symp Proc. .

Abstract

Objective: In a previous study, we investigated a sentence classification model that uses semantic features to extract clinically useful sentences from UpToDate, a synthesized clinical evidence resource. In the present study, we assess the generalizability of the sentence classifier to Medline abstracts.

Methods: We applied the classification model to an independent gold standard of high quality clinical studies from Medline. Then, the classifier trained on UpToDate sentences was optimized by re-retraining the classifier with Medline abstracts and adding a sentence location feature.

Results: The previous classifier yielded an F-measure of 58% on Medline versus 67% on UpToDate. Re-training the classifier on Medline improved F-measure to 68%; and to 76% (p<0.01) after adding the sentence location feature.

Conclusions: The classifier's model and input features generalized to Medline abstracts, but the classifier needed to be retrained on Medline to achieve equivalent performance. Sentence location provided additional contribution to the overall classification performance.

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Figures

Figure 1:
Figure 1:
Average precision, recall and F-measure of the feature-rich sentence classifier on UpToDate (from a previous study 17) and Medline sentences.
Figure 2:
Figure 2:
Average precision, recall and F-measure of the baseline method compared with the feature-rich sentence classifier in different training and testing settings.
Figure 3:
Figure 3:
Average precision, recall and F-measure of the feature-rich sentence classifier, with and without location feature, and trained and tested on Medline sentences.
See this image and copyright information in PMC

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