As the number of electronic biomedical textual resources increases, it becomes harder for physicians to find useful answers at the point of care. Information retrieval applications provide access to databases; however, little research has been done on using automatic summarization to help navigate the documents returned by these systems. After presenting a semantic abstraction automatic summarization system for MEDLINE citations, we concentrate on evaluating its ability to identify useful drug interventions for 53 diseases. The evaluation methodology uses existing sources of evidence-based medicine as surrogates for a physician-annotated reference standard. Mean average precision (MAP) and a clinical usefulness score developed for this study were computed as performance metrics. The automatic summarization system significantly outperformed the baseline in both metrics. The MAP gain was 0.17 (p<0.01) and the increase in the overall score of clinical usefulness was 0.39 (p<0.05).
The authors use the output of SemRep to find the best treatments to target diseases. For this they select the treatments that are linked to the target disease via the TREAT link. The source text is the collection of articles that are result from a search query. The evaluation approach is interesting, since the reference is the list of treatments listed in the Clinical Evidence (CE) and Physicians' Desk Reference (PDR) resources.
%0 Journal Article
%1 Fiszman:2009
%A Fiszman, Marcelo
%A Demner-Fushman, Dina
%A Kilicoglu, Halil
%A Rindflesch, Thomas C.
%D 2009
%J Journal of Biomedical Informatics
%K CAT CAT-SUMMARY evaluation evidence oriented summarization topic
%P 801-813
%T Automatic Summarization of MEDLINE Citations for Evidence-based Medical Treatment: A Topic-oriented Evaluation
%U http://www.ncbi.nlm.nih.gov/pubmed/19022398
%V 42
%X As the number of electronic biomedical textual resources increases, it becomes harder for physicians to find useful answers at the point of care. Information retrieval applications provide access to databases; however, little research has been done on using automatic summarization to help navigate the documents returned by these systems. After presenting a semantic abstraction automatic summarization system for MEDLINE citations, we concentrate on evaluating its ability to identify useful drug interventions for 53 diseases. The evaluation methodology uses existing sources of evidence-based medicine as surrogates for a physician-annotated reference standard. Mean average precision (MAP) and a clinical usefulness score developed for this study were computed as performance metrics. The automatic summarization system significantly outperformed the baseline in both metrics. The MAP gain was 0.17 (p<0.01) and the increase in the overall score of clinical usefulness was 0.39 (p<0.05).
@article{Fiszman:2009,
abstract = {As the number of electronic biomedical textual resources increases, it becomes harder for physicians to find useful answers at the point of care. Information retrieval applications provide access to databases; however, little research has been done on using automatic summarization to help navigate the documents returned by these systems. After presenting a semantic abstraction automatic summarization system for MEDLINE citations, we concentrate on evaluating its ability to identify useful drug interventions for 53 diseases. The evaluation methodology uses existing sources of evidence-based medicine as surrogates for a physician-annotated reference standard. Mean average precision (MAP) and a clinical usefulness score developed for this study were computed as performance metrics. The automatic summarization system significantly outperformed the baseline in both metrics. The MAP gain was 0.17 (p<0.01) and the increase in the overall score of clinical usefulness was 0.39 (p<0.05).},
added-at = {2010-06-16T08:32:10.000+0200},
author = {Fiszman, Marcelo and Demner-Fushman, Dina and Kilicoglu, Halil and Rindflesch, Thomas C.},
biburl = {https://www.bibsonomy.org/bibtex/2df487c8d6c98778e70608084cc262dcd/huiyangsfsu},
interhash = {e32181e3a9b2dee716733fe6daa8df84},
intrahash = {df487c8d6c98778e70608084cc262dcd},
journal = {Journal of Biomedical Informatics},
keywords = {CAT CAT-SUMMARY evaluation evidence oriented summarization topic},
pages = {801-813},
review = {The authors use the output of SemRep to find the best treatments to target diseases. For this they select the treatments that are linked to the target disease via the TREAT link. The source text is the collection of articles that are result from a search query. The evaluation approach is interesting, since the reference is the list of treatments listed in the Clinical Evidence (CE) and Physicians' Desk Reference (PDR) resources.},
timestamp = {2010-11-12T04:42:00.000+0100},
title = {Automatic Summarization of {MEDLINE} Citations for Evidence-based Medical Treatment: A Topic-oriented Evaluation},
url = {http://www.ncbi.nlm.nih.gov/pubmed/19022398},
volume = 42,
year = 2009
}