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Hi-BEHRT: Hierarchical Transformer-Based Model for Accurate Prediction of Clinical Events Using Multimodal Longitudinal Electronic Health Records.

Y. Li, M. Mamouei, G. Salimi-Khorshidi, S. Rao, A. Hassaïne, D. Canoy, T. Lukasiewicz, and K. Rahimi. IEEE J. Biomed. Health Informatics, 27 (2): 1106-1117 (February 2023)

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Deep Bayesian Gaussian Processes for Uncertainty Estimation in Electronic Health Records.Y. Li, S. Rao, A. Hassaïne, R. Ramakrishnan, Y. Zhu, D. Canoy, G. Khorshidi, T. Lukasiewicz, and K. Rahimi. CoRR, (2020)ICDAR 2013 Competition on Gender Prediction from Handwriting.A. Hassaïne, S. Al-Máadeed, J. Al'Jaam, and A. Jaoua. ICDAR, page 1417-1421. IEEE Computer Society, (2013)Authenticity detection as a binary text categorization problem: Application to Hadith authentication.A. Hassaïne, Z. Safi, and A. Jaoua. AICCSA, page 1-7. IEEE Computer Society, (2016)A Set of Geometrical Features for Writer Identification.A. Hassaïne, S. Al-Máadeed, and A. Bouridane. ICONIP (5), volume 7667 of Lecture Notes in Computer Science, page 584-591. Springer, (2012)An Online Signature Verification System for Forgery and Disguise Detection.A. Hassaïne, and S. Al-Máadeed. ICONIP (4), volume 7666 of Lecture Notes in Computer Science, page 552-559. Springer, (2012)An explainable Transformer-based deep learning model for the prediction of incident heart failure.S. Rao, Y. Li, R. Ramakrishnan, A. Hassaïne, D. Canoy, J. Cleland, T. Lukasiewicz, G. Khorshidi, and K. Rahimi. CoRR, (2021)Individual Query Cardinality Estimation using Multiple Query Combinations on a Search Engine's Corpus.F. Islam, A. Hassaïne, A. Jaoua, G. Das, and N. Zhang. ICCA, page 312-316. IEEE, (2017)Uncertainty-Aware Interpretable Deep Learning for Slum Mapping and Monitoring.T. Fisher, H. Gibson, Y. Liu, M. Abdar, M. Posa, G. Salimi-Khorshidi, A. Hassaïne, Y. Cai, K. Rahimi, and M. Mamouei. Remote. Sens., 14 (13): 3072 (2022)Hyper rectangular trend analysis application to Islamic rulings (fatwas).A. Hassaïne, S. Elloumi, F. Ferjani, and A. Jaoua. AICCSA, page 318-325. IEEE Computer Society, (2014)Targeted-BEHRT: Deep learning for observational causal inference on longitudinal electronic health records.S. Rao, M. Mamouei, G. Khorshidi, Y. Li, R. Ramakrishnan, A. Hassaïne, D. Canoy, and K. Rahimi. CoRR, (2022)

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Hi-BEHRT: Hierarchical Transformer-Based Model for Accurate Prediction of Clinical Events Using Multimodal Longitudinal Electronic Health Records.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Hi-BEHRT: Hierarchical Transformer-Based Model for Accurate Prediction of Clinical Events Using Multimodal Longitudinal Electronic Health Records.

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Hi-BEHRT: Hierarchical Transformer-Based Model for Accurate Prediction of Clinical Events Using Multimodal Longitudinal Electronic Health Records.