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Kumar V, Stubbs A, Shaw S, Uzuner Ö. Creation of a new longitudinal corpus of clinical narratives [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S6-S10. https://doi.org/10.1016/j.jbi.2015.09.018

Stubbs A, Kotfila C, Uzuner Ö. Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1 [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S11-S19. https://doi.org/10.1016/j.jbi.2015.06.007

Stubbs A, Uzuner Ö. Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S20-S29. https://doi.org/10.1016/j.jbi.2015.07.020

Yang H, Garibaldi JM. Automatic detection of protected health information from clinic narratives [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S30-S38. https://doi.org/10.1016/j.jbi.2015.06.015

He B, Cheng J, Cen K, Hua W. CRFs based de-identification of medical records [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S39-S46. https://doi.org/10.1016/j.jbi.2015.08.012

Liu Z, Chen Y, Tang B, Wang X, Chen Q, Li H, Wang J, Deng Q, Zhu S. Automatic de-identification of electronic medical records using token-level and character-level conditional random fields [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S47-S52. https://doi.org/10.1016/j.jbi.2015.06.009

Dehghan A, Kovacevic A, Karystianis G, Keane JA, Nenadic G. Combining knowledge- and data-driven methods for de-identification of clinical narratives [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S53-S59. https://doi.org/10.1016/j.jbi.2015.06.029

Chen T, Cullen RM, Godwin M. Hidden Markov model using Dirichlet process for de-identification [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S60-S66. https://doi.org/10.1016/j.jbi.2015.09.004

Stubbs A, Kotfila C, Xu H, Uzuner Ö. Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2 [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S67-S77. https://doi.org/10.1016/j.jbi.2015.07.001

Stubbs A, Uzuner Ö. Annotating risk factors for heart disease in clinical narratives for diabetic patients [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S78-S91. https://doi.org/10.1016/j.jbi.2015.05.009

Kotfila C, Uzuner Ö. A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S92-S102. https://doi.org/10.1016/j.jbi.2015.07.016

Shivade C, Malewadkar P, Fosler-Lussier E, Lai AM. Comparison of UMLS terminologies to identify risk of heart disease using clinical notes [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S103-S110. https://doi.org/10.1016/j.jbi.2015.08.025

Roberts K, Shooshan SE, Rodriguez L, Abhyankar S, Kilicoglu H, Demner-Fushman D. The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S111-S119. https://doi.org/10.1016/j.jbi.2015.06.010

Cormack J, Nath C, Milward D, Raja K, Jonnalagadda SR. Agile text mining for the 2014 i2b2/UTHealth Cardiac risk factors challenge [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S120-S127. https://doi.org/10.1016/j.jbi.2015.06.030

Khalifa A, Meystre S. Adapting existing natural language processing resources for cardiovascular risk factors identification in clinical notes [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S128-S132. https://doi.org/10.1016/j.jbi.2015.08.002

Grouin C, Moriceau V, Zweigenbaum P. Combining glass box and black box evaluations in the identification of heart disease risk factors and their temporal relations from clinical records [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S133-S142. https://doi.org/10.1016/j.jbi.2015.06.014

Urbain J. Mining heart disease risk factors in clinical text with named entity recognition and distributional semantic models [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S143-149. https://doi.org/10.1016/j.jbi.2015.08.009

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