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Titlebook: Computational Science – ICCS 2022; 22nd International C Derek Groen,Clélia de Mulatier,Peter M. A. Sloot Conference proceedings 2022 The Ed

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GAN-Based Data Augmentation for Prediction Improvement Using Gene Expression Data in Cancermonstrate the effectiveness and efficiency of the proposed models. The application of DA methods significantly increase prediction accuracy, leading by 12% with respect to benchmark data sets and 3.15% with respect to data processed with feature selection. Results based on CGAN models outperform in
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National Network for Rare Diseases in Brazil: The Computational Infrastructure and Preliminary Resul Brazil, covering all country regions. We propose collecting, mapping, analyzing data, and supporting effective communication between such centers to share clinical knowledge, evolution, and patient needs, through well-defined and standardized processes. We used validated structures to ensure data p
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Sensitivity Analysis of a Model of Lower Limb Haemodynamicssible for the variation of flow in a vessel where thrombosis is typically observed. When a thrombus was included in the model increase in absolute sensitivity was observed in the leg affected by the thrombosis. These results can be used to inform model reduction strategies and to target clinical dat
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Machine Learning Models for Predicting 30-Day Readmission of Elderly Patients Using Custom Target Eniding data leakage and overfitting. This new risk-score based target encoding approach demonstrated similar performance to existing target encoding and Bayesian encoding approaches, with reduced data leakage, when assessed using Gini-importance. Developed models demonstrated good discriminative perf
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AI Classifications Applied to Neuropsychological Trials in Normal Individuals that Predict Progressimentia (CDRSUM > 2.25), and five other might get questionable impairment (CDRSUM > 0.75). AI methods can find, invisible for neuropsychologists, patterns in cognitive attributes of normal subjects that might indicate their pre-dementia stage.
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Sustainable Energy and Transportationmonstrate the effectiveness and efficiency of the proposed models. The application of DA methods significantly increase prediction accuracy, leading by 12% with respect to benchmark data sets and 3.15% with respect to data processed with feature selection. Results based on CGAN models outperform in
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