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.
Highlights
The critical care sector generates bountiful data around the clock, which can paradoxically complicate the quest for information, knowledge, and ‘wisdom’ [1]
This migrates modeling from the domain expert to the data scientist, often missing the necessary domain expertise, and vice versa, domain experts are not able to perform ad hoc data analyses without the help of experienced analysts. This leads to slow development, adoption and exploitation of highly accurate predictive models, in particular in medical practice, where errors have significant consequences. We address this problem by exploring the potential of visual, code free tools for predictive analytics
RapidMiner processes for automatic building of multiple predictive models, parameter optimization and feature selection are illustrated
Summary
The critical care sector generates bountiful data around the clock, which can paradoxically complicate the quest for information, knowledge, and ‘wisdom’ [1]. Intelligent data analysis hopes for a reduction of cost of care and faster design and implementation of clinical guidelines [3] In this respect, the secondary use of clinical and operational data could support comparative effectiveness research, data mining, and predictive analytics. Successful predictive analytics and application of cutting edge machine learning algorithms often demands substantial programming skills in different languages (e.g. Python or R) This migrates modeling from the domain expert to the data scientist, often missing the necessary domain expertise, and vice versa, domain experts are not able to perform ad hoc data analyses without the help of experienced analysts. The process compared several learning methods (Decision Stump, Decision Tree, Naive Bayes, Logistic Regression, Random Forest, AdaBoost, Bagging, Stacking, Support Vector Machine) in association with feature weighting and selection quantitatively assessed in terms of Correlation, Gini Selection, Information Gain and ReliefF
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