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Can We Classify the Participants of a Longitudinal Epidemiological Study from Their Previous Evolution?

, , , , and . CBMS, page 121-126. IEEE Computer Society, (2015)

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ICE: Interactive Classification Rule Exploration on Epidemiological Data, , , , and . Proc. of the 30th IEEE Int. Symposium on Computer-Based Medical Systems (CBMS17), page 606-611. Thessaloniki, Greece, (2017)Predicting Document Polarities on a Stream without Reading their Contents, , , , and . Proceedings of the Symposium on Applied Computing (SAC), (2018)Towards a unification of treatments and interventions for tinnitus patients: The EU research and innovation action UNITI, , , , , , , , , and 70 other author(s). 260, page 441-451. Elsevier, (2021)Visual Analysis of Missing Values in Longitudinal Cohort Study Data., , , , , , , and . Comput. Graph. Forum, 39 (1): 63-75 (2020)Can We Classify the Participants of a Longitudinal Epidemiological Study from Their Previous Evolution?, , , , and . CBMS, page 121-126. IEEE Computer Society, (2015)Subpopulation Discovery in Epidemiological Data with Subspace Clustering, , , and . Foundations of Computing and Decision Sciences (FCDS), 39 (4): 271-300 (2014)3D Regression Heat Map Analysis of Population Study Data, , , , , , and . IEEE Transactions on Visualization and Computer Graphics (TVCG), 22 (1): 81-90 (2015)Gender-Specific Differences in Patients With Chronic Tinnitus—Baseline Characteristics and Treatment Effects, , , , and . Frontiers in Neuroscience, (2020)A cost-based multi-layer network approach for the discovery of patient phenotypes., , , and . CoRR, (2022)Predicting polarities of entity-centered documents without reading their contents., , , , and . SAC, page 525-528. ACM, (2018)