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

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.

The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison

“…A further limitation is that, for simplicity, only weather information from the capitals were used in the study, but for countries like Japan, Thailand and Taiwan, there are great variation between climatic conditions in the North and the South. Prediction accuracy might improve if [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] incidence and weather information can be collected at a finer resolution. We suspect that even the accuracy of short term forecasts may be reduced should new epidemiological conditions replace those that the model was trained on.…”

“…While the response to endemic diseases may be less urgent, the burden caused by pathogens such as influenza or malaria is high [9][10][11], and due to their endemicity, many countries have long standing surveillance systems to track outbreaks and guide response, from vector control to hospital bed utilization [12][13][14][15]. Early warning systems aiming to predict epidemics as soon as possible can allow control methods to be carried out rapidly and increase their chance of success [16,17]. To do so, decision makers need to be able to make accurate forecasts of incidence and to automate this forecasting process based on routinely collected notification data [18].…”

Early Prediction of Patient Mortality Based on Routine Laboratory Tests and Predictive Models in Critically Ill Patients

“…The process compared different learning and ensemble methods (Decision Stump, Decision Tree, Naive Bayes, Logistic Regression (LR), Random Forest, Support Vector Machine, AdaBoost, Bagging, and Stacking) in association with feature weighting and selection, quantitatively assessed in terms of Correlation, Gini Selection, and Information Gain and ReliefF as previously described [8].…”

“…A more detailed technical description of the use of RapidMiner for scalable predictive analytics of medical data, as well as templates of generic processes, can be found in [8] and its supplementary materials.…”

“…Based on previous findings, we compared the predictive performance of different single (Decision Tree, Naive Bayes, Logistic and Regression) and ensemble (Random Forest, Boosting, and Bagging) learning methods. Moreover, the predictive power and importance of predictors (laboratory tests) were quantitatively assessed by use of feature weighting and selection techniques: Correlation, Gini Selection, Information Gain and ReliefF [8]. For predictive modeling, feature selection, and visual analytics of the results, we used RapidMiner and R platforms as mentioned in [9][10][11].…”