Data mining for better healthcare: A path towards automated data analysis?

Abstract

In today's world, large volumes of medical data are being continuously generated, but their value is severely undermined by our inability to translate them into knowledge and, ultimately, actions. Data mining techniques allow the extraction of previously unknown interesting patterns from large datasets, but their complexity limits their practical diffusion. Data-driven analysis is a multi-step process, in which health care professionals define analysis goals and assess extracted knowledge, while computer scientists tackle the non trivial task of driving the miner system analysis activity. This paper addresses the mining activity from a different perspective. We believe that mining systems should be able to devise which knowledge could be most interesting to users and extract actionable knowledge from large medical datasets, with minimal user intervention. More specifically, mining systems should be capable of (i) devising viable end-goals for a specific dataset (i.e., that yield actionable knowledge to the user and are feasible given the dataset characteristics and size), based on the expertise acquired during the analysis of previous datasets, and (ii) extracting a manageable set of knowledge. Automated data analysis should fuel the next generation of medical mining systems, thus enabling users to automatically mine massive data repositories.