The application of data mining techniques to the medical and biological domain has gained great interest in the last few years, also thanks to the encouraging results achieved in many fields. One issue of particular interest in this area is represented by the analysis of temporal data, usually referred to as Temporal Data Mining (TDM). Within TDM, research focuses on the analysis of time series, collected measuring clinical or biological variables at different points in time.

One of the most attractive applications of AI-based TDM concerns the extraction of temporal rules from data. When applied to the biomedical domain, this could be of particular interest, for example in reconstructing gene regulatory networks or in discovering knowledge about the causes of a target event.

We developed two algorithms for the mining of temporal rules from data.

Several clinical studies have shown that the “Failure to Adhere” to the hemodialysis treatment plan is related to a worsening of the long-term outcomes in dialysis patients. We are currently working on a software tool, which is able to automatically collect the hemodialisys data, to compare them with the prescriptions and to highlight the reasons of “the failures to adhere” by statistical analysis. The software design follows the principles of data mining tools, thus enabling the user to “drill down” the data, starting from the dialysis-centre view and moving to the single patient analysis.

A significant number of preventable cardiac deaths in infancy and childhood is due to Long QT Syndrome (LQTS) and to unrecognised neonatal congenital heart diseases (CHDs). A decision analysis approach was used to explore the cost-effectiveness of introducing a neonatal ECG screening to discover patients at risk and take opportune prevention measures.