Finding temporal order in discharge summaries. Export

@article{citeulike:2857888, abstract = {A method for automatic analysis of time-oriented clinical narratives would be of significant practical import for medical decision making, data modeling and biomedical research. This paper proposes a robust corpus-based approach for temporal analysis of medical discharge summaries. We characterize temporal organization of clinical narratives in terms of temporal segments and their ordering. We consider a temporal segment to be a fragment of text that does not exhibit abrupt changes in temporal focus. Our method derives temporal order based on a range of linguistic and contextual features that are integrated in a supervised machine-learning framework. Our learning method achieves 83\% F-measure in tempo-ral segmentation, and 78.3\% accuracy in inferring pairwise temporal relations.}, address = {Massachusetts Institute of Technology (MIT), Cambridge, MA, USA.}, author = {Bramsen, P. and Deshpande, P. and Lee, Y. K. and Barzilay, R.}, citeulike-article-id = {2857888}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/17238307}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=17238307}, issn = {1559-4076}, journal = {AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium}, keywords = {all, annotation, ie, nlp, temporal-annotation}, pages = {81--85}, posted-at = {2008-09-12 15:50:47}, priority = {5}, title = {Finding temporal order in discharge summaries.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/17238307}, year = {2006} }

TY - JOUR ID - citeulike:2857888 L3 - citeulike-article-id:2857888 N2 - A method for automatic analysis of time-oriented clinical narratives would be of significant practical import for medical decision making, data modeling and biomedical research. This paper proposes a robust corpus-based approach for temporal analysis of medical discharge summaries. We characterize temporal organization of clinical narratives in terms of temporal segments and their ordering. We consider a temporal segment to be a fragment of text that does not exhibit abrupt changes in temporal focus. Our method derives temporal order based on a range of linguistic and contextual features that are integrated in a supervised machine-learning framework. Our learning method achieves 83% F-measure in tempo-ral segmentation, and 78.3% accuracy in inferring pairwise temporal relations. JF - AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium SN - 1559-4076 AD - Massachusetts Institute of Technology (MIT), Cambridge, MA, USA. EP - 85 TI - Finding temporal order in discharge summaries. SP - 81 KW - all KW - annotation KW - ie KW - nlp KW - temporal-annotation AU - Bramsen, P AU - Deshpande, P AU - Lee, YK AU - Barzilay, R PY - 2006/// UR - http://view.ncbi.nlm.nih.gov/pubmed/17238307 ER -