![]() ![]() The potential of predictive analytics is to provide the clinical end-user with validated medical decision support and ultimately leading to more Predictive, Preventive and Personalized Medicine-PPPM. The clinical end-user is not in the position to constantly monitor and process the large amounts of data generated by patient monitoring and diagnostics. Commonly used data analysis platforms in clinical practice, frequently only provide support for data integration and monitoring, leaving all the analysis and decision taking to the clinical end-users. In this respect, the secondary use of clinical and operational data could support comparative effectiveness research, data mining, and predictive analytics. Additionally intelligent data analysis hopes for a reduction of cost of care and faster design and implementation of clinical guidelines. Intelligent data analysis promises a more efficient representation of the complex relations between symptoms, diseases and treatment. The accumulation of clinical data has outpaced the capacity for effective aggregation and analysis aiming to support clinical quality, patient safety and integrated patient care. The critical care sector generates bountiful data around the clock, which can paradoxically complicate the quest for information, knowledge, and ‘wisdom’. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research. 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. As use case, correlation of platelet count and ICU survival was quantitatively assessed. 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 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). Moreover, we review code free applications of big data technologies. In this study, the authors address this problem by focusing on open, visual environments, suited to be applied by the medical community. This leaves a gap between potential and actual data usage. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. However, high-dimensionality and high-complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. 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.
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