Best variable identification by means of data-mining and cooperative game theory

To develop and evaluate methods to assess single and grouped variables impact on measuring intervention severities and support a search for most expressive variables.

Datasets of cohort studies are analyzed automatically based on algorithms. For this, a metric is developed to compare measured variables in different cohorts in a data-mining process. Variables are measured in all possible combinations to detect possible synergies of certain variable constellations and allow for a ranking of the combinations' expressiveness. Such ranking serves as a basis for a wide range of algorithmic data analysis. In an exemplary application, every group member's impact on the total result is determined based on the principle of the cooperative game theory besides to the total expressiveness of the variable groups.

For different types of interventions, the method is applied to experimental data containing multiple recorded medical lab values. The expressiveness of variable combinations to indicate severity is ranked by means of a metric. Within each combination, any variable's contribution to the total effect is determined and accumulated over whole datasets to yield local and global variable importance measures. The computed results have been successfully matched with clinical expectations to prove their plausibility.

Algorithmic evaluation shows to be a promising approach in automatized quantification of variable expressiveness. It can assess descriptive power of measurements, help to improve future study designs and expose worthwhile research issues.