Intelligent decision support systems for clinical data patient analysis through data mining techniques

Abstract

The high growth of clinical data sets originated from the introduction of electronic health records
collected in medical environments. Nowadays, the computerization of clinical data is supported by
many international projects and frequently national integrated clinical record systems are adopted
in both public and private health-care facilities. The study of the diseases and the discovery of
effective therapies demands collection, management, integration and analysis of clinical data. The
main target is to obtain valuable knowledge from large sets of clinical data. Novel methods for their
analysis are therefore required in order to extract relevant information and compact models. A
candidate discipline is data mining, an interdisciplinary field that comprises computer science,
statistics and artificial intelligence, and is dedicated to automate the knowledge discovery process.
Data mining methods are able to consider different types of data - structured and unstructured -
from disparate sources. The objectives of clinical data mining are to: recognize the clinical and
laboratory variables that characterize a particular disease; deal with often incomplete (missing
values) and noisy data sets (e.g. different measure scales); integrate patient samples from different
sources; recognize variables name with the same meaning; integrate diverse patient data collection
procedures. The management of clinical data, the discovery of patients interactions, and the
integration of the disparate data sources are the hardest problems to solve. Finally, after an
adequate handling of these issues, a compact and human understandable data model has to be
extracted. In this work, consolidated data mining methods (e.g., artificial neural networks, decision
trees, rule based classifiers) able to manage and analyze clinical data sets are introduced and
applied to a real case study. Demented patient samples collected in different Italian health care
facilities are investigated, providing a practical example of clinical data mining. Classification
through artificial neural networks, logic rules, decision trees are considered and compared. It is
shown that supervised classification is a promising technique to identify the disease of the patients
and to extract significant models for biomedical knowledge discovery.