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Data-Driven Visual Characterization of Patient Health-Status Using Electronic Health Records and Self-Organizing Maps.

, , , , and . IEEE Access, (2020)

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Interpreting clinical latent representations using autoencoders and probabilistic models., , , and . Artif. Intell. Medicine, (2021)Clinical Synthetic Data Generation to Predict and Identify Risk Factors for Cardiovascular Diseases., , , , , , , and . Poly/DMAH@VLDB, volume 13814 of Lecture Notes in Computer Science, page 75-91. Springer, (2022)Data-Driven Visual Characterization of Patient Health-Status Using Electronic Health Records and Self-Organizing Maps., , , , and . IEEE Access, (2020)Data-driven cardiovascular risk prediction and prognosis factor identification in diabetic patients., , , , , and . BHI, page 1-4. IEEE, (2022)On the Use of Generative Adversarial Networks to Predict Health Status Among Chronic Patients., , , , and . HEALTHINF, page 167-178. SCITEPRESS, (2023)Characterizing Cardiovascular Risk Through Unsupervised and Interpretable Techniques., , and . IDEAL, volume 13756 of Lecture Notes in Computer Science, page 22-30. Springer, (2022)Learning and visualizing chronic latent representations using electronic health records., , , and . BioData Min., (2022)