Publications

2016
Kholghi M, Sitbon L, Zuccon G, Nguyen A. Active learning: a step towards automating medical concept extraction [Internet]. Journal of the American Medical Informatics Association 2016;23(2):289-296. https://doi.org/10.1093/jamia/ocv069
See also: Post-challenge
Lin C, Dligach D, Miller TA, Bethard S, Savova GK. Multilayered temporal modeling for the clinical domain [Internet]. Journal of the American Medical Informatics Association 2016;23(2):387-395. https://doi.org/10.1093/jamia/ocv113
See also: Post-challenge
2015
Uzuner Ö, Stubbs A. Practical applications for natural language processing in clinical research: The 2014 i2b2/UTHealth shared tasks [Internet]. Journal of Biomedical Informatics 2015;58(Supplement):S1-S5. https://doi.org/10.1016/j.jbi.2015.10.007
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

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