Knowledge Discovery Approaches for Early Detection of Decompensation Conditions in Heart Failure Patients

Abstract

A crucial mid-long term goal for the clinical management of chronic heart failure (CHF) patients is to detect in advance new decompensation events, for improving quality of outcomes while reducing costs on the healthcare system. Within the relevant clinical protocols and guidelines, a general consensus has not been reached on how further decompensations could be predicted, even though many different evidence-based indications are known. In this paper we present the Knowledge Discovery (KD) task which has been implemented and developed into the EU FP6 Project HEARTFAID (www.heartfaid.org), proposing an innovative knowledge based platform of services for effective and efficient clinical management of heart failure within elderly population. KD approaches have represented a practical and effective tool for analyzing data about 49 CHF patients who have been recurrently visited by cardiologist, measuring clinical parameters taken from clinical guidelines and evidence-based knowledge and that are also easy to be acquired at home setting. Several KD algorithms have been applied on collected data, obtaining different binary classifiers performing a plausible early detection of new decompensations, showing high accuracy on internal validation and independent test.