Mining Clinical, immunological, and genetic data of solid organ transplantation

Clinical databases store large amounts of information about patients and their medical conditions. Data mining techniques can extract relationships and patterns implicit in this wealth of data, and thus be helpful in understanding the progression of diseases and the efficacy of the associated therapies. In this perspective, in Pisa (Italy) we have started an important data collection and analysis project, where a very large number of epidemiological, clinical, immunological and genetic variables collected before the transplantation of a solid organ, and during the follow-up assessment of the patients, are stored in a datawarehouse for future mining. This on-going data collection involves all liver, kidney, pancreas and kidney-pancreas transplantations of the last five years of one of the largest (as to number of transplantations) centers in Europe. The project ambitious goal is to gain deeper insights in all the phenomena related to solid organ transplantation, with the aim of improving the donor-recipient matching policy used nowadays. In this chapter we report in details two different data mining activities developed within this project. The first analysis involves mining genetic data of patients affected by terminal hepatic cirrhosis with viral origin (HCV and HBV) and patients with terminal hepatic cirrhosis with non-viral origin (autoimmune): the goal is to assess the influence of the HLA antigens on the course of the disease. In particular, we have evaluated if some genetic configurations of the class I and class II HLA are significantly associated with the triggering causes of the hepatic cirrhosis. The second analysis involves clinical data of a set of patients in the follow-up of a liver transplantation. The aim of the data analysis is that of assessing the effectiveness of the extracorporeal photopheresis (ECP) as a therapy to prevent rejection in solid organ transplantation. For both analyses we describe in details, the medical context and goal, the nature and structure of the data. We also discuss which kind of data mining technique is the most suitable for our purposes, and we describe the details of the knowledge discovery process followed and extracted knowledge.