Clinical Data Mining: Problems, Pitfalls and Solutions

The wide spread of electronic data collection in medical environments leads to an exponential growth of clinical data extracted from heterogeneous patient samples. Collecting, managing, integrating and analyzing these data are essential activities in order to shed light on diseases and on related therapies. The major issues in clinical data analysis are the incompleteness (missing values), the different adopted measure scales, the integration of the disparate collection procedures. Therefore, the main challenges are in managing clinical data, in discovering patients interactions, and in integrating the different data sources. The final goal is to extract relevant information from huge amounts of clinical data. Therefore, the analysis of clinical data requires new effective and efficient methods to extract compact and relevant information: the interdisciplinary field of data mining, which guides the automated knowledge discovery process, is a natural way to approach the complex task of clinical data analysis. Data mining deals with structured and unstructured data, that are, respectively, data for which we can give a model or not. For example, in clinical contexts it is important to highlight those trials (variables) that are frequent in a particular disease diagnosis. The objective of this work is to study and apply methods to manage and retrieve relevant information in clinical data sets. A practical analysis from real patient data collected from several dementia clinical departments in Italy is reported as example of clinical data mining. The particular field of logic classification, where a data model is computed in form of propositional logic formulas, is investigated for clinical data mining and compared to other techniques, showing that it is a successful approach to compute a compact data model for clinical knowledge discovery.