A Clinical Decision Support System based on fuzzy rules and classification algorithms for monitoring the physiological parameters of type-2 diabetic patients

The use of different types of Clinical Decision Support Systems (CDSS) makes possible the improvement of the quality of the therapeutic and diagnostic efficiency in health field. Those systems, properly implemented, are able to simulate human expert clinician reasoning in order to suggest decisions on treatment of patients. In this paper, we exploit fuzzy inference machines to improve the quality of the day-by-day clinical care of type-2 diabetic patients of Anti-Diabetes Centre (CAD) of the Local Health Authority ASL Naples 1 (Naples, Italy). All the designed functionalities were developed thanks to the experience on the field, through different phases (data collection and adjustment, Fuzzy Inference System development and its validation on real cases) executed by an interdisciplinary research team comprising doctors, clinicians and IT engineers. The proposed approach also allows the remote monitoring of patients' clinical conditions and, hence, can help to reduce hospitalizations

Multisensor Data Fusion and Decision Support in Wireless Body Sensor Networks

Maintaining and improving the quality of life in ageing populations is a necessity. Hence, distant patient monitoring is a solution providing constant surveillance of vital signs and the detection of emergencies when they occur. In the past few years, wireless body sensor networks (WBSNs) emerged as a low cost solution for healthcare applications. In WBSNs, biosensors collect periodically physiological measures and send them to the coordinator where the data fusion process takes place. However, processing the huge amount of data captured by the limited lifetime biosensors and taking the right decisions when there is an emergency are major challenges in WBSNs. In this paper, we introduce a data fusion model using a decision matrix, an early warning score system and fuzzy set theory. We propose an algorithm at the coordinator level of the WBSN, aiming to take the appropriate decision when an emergency is detected