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Medical Concept Embedding with Multiple Ontological Representations.

, , , , , and . IJCAI, page 4613-4619. ijcai.org, (2019)

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Learning Inter-Modal Correspondence and Phenotypes from Multi-Modal Electronic Health Records., , , and . CoRR, (2020)Context-Aware Imputation for Clinical Time Series., and . ICHI, page 1-3. IEEE, (2019)DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistency., , , , and . AAAI, page 16416-16424. AAAI Press, (2024)DNA-T: Deformable Neighborhood Attention Transformer for Irregular Medical Time Series., , , and . IEEE J. Biomed. Health Informatics, 28 (7): 4224-4237 (July 2024)Joint Learning of Phenotypes and Diagnosis-Medication Correspondence via Hidden Interaction Tensor Factorization., , , , and . IJCAI, page 3627-3633. ijcai.org, (2018)Medical Concept Embedding with Multiple Ontological Representations., , , , , and . IJCAI, page 4613-4619. ijcai.org, (2019)Adaptive Integration of Categorical and Multi-relational Ontologies with EHR Data for Medical Concept Embedding., , , , and . ACM Trans. Intell. Syst. Technol., 14 (6): 111:1-111:20 (December 2023)SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors., , , , , , and . AAAI, page 6548-6556. AAAI Press, (2021)LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values., , , , , and . KDD, page 1625-1635. ACM, (2020)TedPar: Temporally Dependent PARAFAC2 Factorization for Phenotype-based Disease Progression Modeling., , , and . SDM, page 594-602. SIAM, (2021)