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Transformation of Temperature Timeseries into Features that Characterize Patients with Diabetic Autonomic Nerve Disorder.

, , , , , , and . CBMS, page 65-70. IEEE Computer Society, (2018)

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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)Entity-Level Stream Classification: Exploiting Entity Similarity to Label the Future Observations Referring to an Entity., , , , , , , and . DSAA, page 246-255. IEEE, (2018)Predicting polarities of entity-centered documents without reading their contents., , , , and . SAC, page 525-528. ACM, (2018)Transformation of Temperature Timeseries into Features that Characterize Patients with Diabetic Autonomic Nerve Disorder., , , , , , and . CBMS, page 65-70. IEEE Computer Society, (2018)Interactive Medical Miner: Interactively Exploring Subpopulations in Epidemiological Datasets., , , and . ECML/PKDD (3), volume 8726 of Lecture Notes in Computer Science, page 460-463. Springer, (2014)Learning and inspecting classification rules from longitudinal epidemiological data to identify predictive features on hepatic steatosis., , , and . Expert Syst. Appl., 41 (11): 5405-5415 (2014)A framework for expert-driven subpopulation discovery and evaluation using subspace clustering for epidemiological data., , , , , and . Expert Syst. Appl., (2018)Visual Subpopulation Discovery and Validation in Cohort Study Data., , , , , , , and . CoRR, (2017)Exploiting entity information for stream classification over a stream of reviews., , , , , and . SAC, page 564-573. ACM, (2019)Building a Bayesian Network to Understand the Interplay of Variables in an Epidemiological Population-Based Study., , , , , , and . CBMS, page 88-93. IEEE Computer Society, (2018)