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39 results on '"Vukicevic, Milan"'

2. Two Sides of Collective Decision Making - Votes from Crowd and Knowledge from Experts

Author

Dodevska, Zorica A., Kovacevic, Ana, Vukicevic, Milan, Delibašić, Boris, van der Aalst, Wil, Series Editor, Mylopoulos, John, Series Editor, Rosemann, Michael, Series Editor, Shaw, Michael J., Series Editor, Szyperski, Clemens, Series Editor, Moreno-Jiménez, José María, editor, Linden, Isabelle, editor, Dargam, Fatima, editor, and Jayawickrama, Uchitha, editor

Published
2020
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3. CrEx-Wisdom Framework for Fusion of Crowd and Experts in Crowd Voting Environment – Machine Learning Approach

Author

Kovacevic, Ana, Vukicevic, Milan, Radovanovic, Sandro, Delibasic, Boris, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Bellatreche, Ladjel, editor, Bieliková, Mária, editor, Boussaïd, Omar, editor, Catania, Barbara, editor, Darmont, Jérôme, editor, Demidova, Elena, editor, Duchateau, Fabien, editor, Hall, Mark, editor, Merčun, Tanja, editor, Novikov, Boris, editor, Papatheodorou, Christos, editor, Risse, Thomas, editor, Romero, Oscar, editor, Sautot, Lucile, editor, Talens, Guilaine, editor, Wrembel, Robert, editor, and Žumer, Maja, editor

Published
2020
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7. An Investigation of Human Trajectories in Ski Resorts

Author

Delibašić, Boris, Radovanović, Sandro, Jovanović, Miloš, Vukićević, Milan, Suknović, Milija, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Yuan, Junsong, Series editor, Zhou, Lizhu, Series editor, Trajanov, Dimitar, editor, and Bakeva, Verica, editor

Published
2017
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21. Normalization methods in time series of platelet function assays

Author

Van Poucke, Sven, primary, Zhang, Zhongheng, additional, Roest, Mark, additional, Vukicevic, Milan, additional, Beran, Maud, additional, Lauwereins, Bart, additional, Zheng, Ming-Hua, additional, Henskens, Yvonne, additional, Lancé, Marcus, additional, and Marcus, Abraham, additional

Published
2016
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36. Extending meta-learning framework for clustering gene expression data with component-based algorithm design and internal evaluation measures

Author

Vukicevic, Milan, Radovanovic, Sandro, Delibasic, Boris, and Suknovic, Milija

Abstract

Class retrieval in gene expression microarray data analysis is highly challenging task. Because of high class imbalance, highly dimensional feature space and small number of samples most of the algorithms fail to capture real complex structures in data ('golden standard'). Therefore, one of the major problems in this area is selection of the best suited algorithm for data at hand. We address this problem by proposing an extended meta-learning framework for ranking and selection of algorithms for clustering gene expression microarray data. Proposed framework introduces several improvements compared to the original one: extended algorithm space, extended meta-feature space and introduction of cutting edge techniques for meta-feature selection and parameter optimisation of meta-algorithms. System was evaluated on large algorithm and problem space (504 algorithms and 30 datasets) and showed very promising results in prediction of algorithm performance for specific problems.

Published
2016
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38. Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform

Author

Margot Vander Laenen, Sven Van Poucke, Leo Anthony Celi, Cathy De Deyne, Milan Vukicevic, Zhongheng Zhang, Martin Schmitz, VAN POUCKE, Sven, Zhang, Zhongheng, Schmitz, Martin, Vukicevic, Milan, VANDER LAENEN, Margot, Celi, Leo Anthony, DE DEYNE, Cathy, Institute for Medical Engineering and Science, and Celi, Leo Anthony G.

Subjects
Critical Care, Databases, Factual, 020205 medical informatics, Process (engineering), Computer science, Critical Illness, Big data, Information Storage and Retrieval, lcsh:Medicine, 02 engineering and technology, computer.software_genre, 0202 electrical engineering, electronic engineering, information engineering, Data Mining, Humans, lcsh:Science, Multidisciplinary, business.industry, Data manipulation language, lcsh:R, Reproducibility of Results, Models, Theoretical, Predictive analytics, 3. Good health, Intensive Care Units, Open data, Analytics, Scalability, Programming Languages, 020201 artificial intelligence & image processing, lcsh:Q, Data mining, business, computer, Algorithms, Research Article
Abstract

With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high-complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. In this study, the authors address this problem by focusing on open, visual environments, suited to be applied by the medical community. Moreover, we review code free applications of big data technologies. As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner’s Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. As use case, correlation of platelet count and ICU survival was quantitatively assessed. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, we developed robust processes for automatic building, parameter optimization and evaluation of various predictive models, under different feature selection schemes. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research., National Institutes of Health (U.S.) (National Institute for Biomedical Imaging and Bioengineering (U.S.) Grant R01 EB01720501A1)

Published
2016

39. Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform.

Author

Van Poucke S, Zhang Z, Schmitz M, Vukicevic M, Laenen MV, Celi LA, and De Deyne C

Subjects
Algorithms, Data Mining methods, Databases, Factual statistics & numerical data, Humans, Information Storage and Retrieval methods, Intensive Care Units statistics & numerical data, Models, Theoretical, Programming Languages, Reproducibility of Results, Critical Care statistics & numerical data, Critical Illness therapy, Data Mining statistics & numerical data, Information Storage and Retrieval statistics & numerical data
Abstract

With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high-complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. In this study, the authors address this problem by focusing on open, visual environments, suited to be applied by the medical community. Moreover, we review code free applications of big data technologies. As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner's Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. As use case, correlation of platelet count and ICU survival was quantitatively assessed. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, we developed robust processes for automatic building, parameter optimization and evaluation of various predictive models, under different feature selection schemes. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research.

Published
2016
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