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http://hdl.handle.net/1942/20677
Title: | Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform | Authors: | VAN POUCKE, Sven Zhang, Zhongheng Schmitz, Martin Vukicevic, Milan VANDER LAENEN, Margot Celi, Leo Anthony DE DEYNE, Cathy |
Issue Date: | 2016 | Publisher: | PUBLIC LIBRARY SCIENCE | Source: | PLOS ONE, 11 (1) | 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. | Notes: | [Van Poucke, Sven; Vander Laenen, Margot; De Deyne, Cathy] Ziekenhuis Oost Limburg, Dept Anesthesiol Intens Care Emergency Med & Pain, Genk, Belgium. [Zhang, Zhongheng] Zhejiang Univ, Jinhua Hosp, Dept Crit Care Med, Hangzhou, Zhejiang, Peoples R China. [Schmitz, Martin] RapidMiner GmbH, Dortmund, Germany. [Vukicevic, Milan] Univ Belgrade, Dept Org Sci, Belgrade, Serbia. [Celi, Leo Anthony] MIT, Inst Med Engn & Sci, Cambridge, MA 02139 USA. [De Deyne, Cathy] Univ Hasselt, Fac Med, Limburg Clin Res Program, Hasselt, Belgium. | Document URI: | http://hdl.handle.net/1942/20677 | ISSN: | 1932-6203 | e-ISSN: | 1932-6203 | DOI: | 10.1371/journal.pone.0145791 | ISI #: | 000367801400054 | Rights: | Copyright: © 2016 Poucke et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2017 |
Appears in Collections: | Research publications |
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van poucke 1.pdf | Published version | 826.73 kB | Adobe PDF | View/Open |
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