Publications

2015
Chang N-W, Dai H-J, Jonnagaddala J, Chen C-W, Tsai RT-H, Hsu W-L. A context-aware approach for progression tracking of medical concepts in electronic medical records [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S150-S157. https://doi.org/10.1016/j.jbi.2015.09.013
Chen Q, Li H, Tang B, Wang X, Liu X, Liu Z, Liu S, Wang W, Deng Q, Zhu S, Chen Y, Wang J. An automatic system to identify heart disease risk factors in clinical texts over time [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S158-S163. https://doi.org/10.1016/j.jbi.2015.09.002
Torii M, Fan J-wei, Yang W-li, Lee T, Wiley MT, Zisook DS, Huang Y. Risk factor detection for heart disease by applying text analytics in electronic medical records [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S164-S170. https://doi.org/10.1016/j.jbi.2015.08.011
Yang H, Garibaldi JM. A hybrid model for automatic identification of risk factors for heart disease [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S171-182. https://doi.org/10.1016/j.jbi.2015.09.006
Karystianis G, Dehghan A, Kovacevic A, Keane JA, Nenadic G. Using local lexicalized rules to identify heart disease risk factors in clinical notes [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S183-S188. https://doi.org/10.1016/j.jbi.2015.06.013
Zheng K, Vydiswaran VGV, Liu Y, Wang Y, Stubbs A, Uzuner Ö, Gururaj AE, Bayer S, Aberdeen J, Rumshisky A, Pakhomov S, Liu H, Xu H. Ease of adoption of clinical natural language processing software: An evaluation of five systems [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S189-S196. https://doi.org/10.1016/j.jbi.2015.07.008
Solomon JW, Nielsen RD. Predicting changes in systolic blood pressure using longitudinal patient records [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S197-S202. https://doi.org/10.1016/j.jbi.2015.06.024
Jonnagaddala J, Liaw S-T, Ray P, Kumar M, Chang N-W, Dai H-J. Coronary artery disease risk assessment from unstructured electronic health records using text mining [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S203-S210. https://doi.org/10.1016/j.jbi.2015.08.003
Shivade C, Hebert C, Lopetegui M, de Marneffe M-C, Fosler-Lussier E, Lai AM. Textual inference for eligibility criteria resolution in clinical trials [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S211-S218. https://doi.org/10.1016/j.jbi.2015.09.008
Pradhan S, Elhadad N, South BR, Martinez D, Christensen L, Vogel A, Suominen H, Chapman WW, Savova G. Evaluating the state of the art in disorder recognition and normalization of the clinical narrative [Internet]. Journal of the American Medical Informatics Association 2015;22(1):143-154. https://doi.org/10.1136/amiajnl-2013-002544
See also: Post-challenge
Jung K, LePendu P, Iyer S, Bauer-Mehren A, Percha B, Shah NH. Functional evaluation of out-of-the-box text-mining tools for data-mining tasks [Internet]. Journal of the American Medical Informatics Association 2015;22(1):121-131. https://doi.org/10.1136/amiajnl-2014-002902
See also: Post-challenge
Lin C-H, Wu N-Y, Lai W-S, Liou D-M. Comparison of a semi-automatic annotation tool and a natural language processing application for the generation of clinical statement entries [Internet]. Journal of the American Medical Informatics Association 2015;22(1):132-142. https://doi.org/10.1136/amiajnl-2014-002991
See also: Post-challenge
Sun W, Rumshisky A, Uzuner Ö. Normalization of relative and incomplete temporal expressions in clinical narratives [Internet]. Journal of the American Medical Informatics Association 2015;22(5):1001-1008. https://doi.org/10.1093/jamia/ocu004
See also: Post-challenge
2013
Sun W, Rumshisky A, Uzuner Ö. Evaluating temporal relations in clinical text: 2012 i2b2 Challenge [Internet]. Journal of the American Medical Informatics Association 2013;20(5):806-813. https://doi.org/10.1136/amiajnl-2013-001628
Sun W, Rumshisky A, Uzuner Ö. Temporal reasoning over clinical text: the state of the art [Internet]. Journal of the American Medical Informatics Association 2013;20(5):814-819. https://doi.org/10.1136/amiajnl-2013-001760
Grouin C, Grabar N, Hamon T, Rosset S, Tannier X, Zweigenbaum P. Eventual situations for timeline extraction from clinical reports [Internet]. Journal of the American Medical Informatics Association 2013;20(5):820-827. https://doi.org/10.1136/amiajnl-2013-001627
Tang B, Wu Y, Jiang M, Chen Y, Denny JC, Xu H. A hybrid system for temporal information extraction from clinical text [Internet]. Journal of the American Medical Informatics Association 2013;20(5):828-835. https://doi.org/10.1136/amiajnl-2013-001635
Sohn S, Wagholikar K, Li D, Jonnalagadda SR, Tao C, Elayavilli RK, Liu H. Comprehensive temporal information detection from clinical text: medical events, time, and TLINK identification [Internet]. Journal of the American Medical Informatics Association 2013;20(5):836-842. https://doi.org/10.1136/amiajnl-2013-001622
Cherry C, Zhu X, Martin J, de Bruijn B. À la Recherche du Temps Perdu: extracting temporal relations from medical text in the 2012 i2b2 NLP challenge [Internet]. Journal of the American Medical Informatics Association 2013;20(5):843-848. https://doi.org/10.1136/amiajnl-2013-001624
Xu Y, Wang Y, Liu T, Tsujii J, Chang EI-C. An end-to-end system to identify temporal relation in discharge summaries: 2012 i2b2 challenge [Internet]. Journal of the American Medical Informatics Association 2013;20(5):849-858. https://doi.org/10.1136/amiajnl-2012-001607

Pages