2014 - Deidentification & Heart Disease

2015
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

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