Using fuzzy logic for improving clinical daily-care of β-thalassemia patients

The domain of medical decision making process is heavily affected by vagueness and uncertainty issues and — for copying with them — different type of Clinical Decision Support System (CDSS)s, simulating human expert clinician reasoning, have been designed in order to suggest decisions on treatment of patients. In this paper, we exploit fuzzy inference machines to improve the knowledge-based CDSS actually used in the day-by-day clinical care of β-thalassemia patients of the Rare Red Blood Cell Disease Unit (RRBCDU) at Cardarelli Hospital (Naples, Italy). All the designed functionalities were iteratively developed on the field, through requirement-adjustment/development/validation cycles executed by an interdisciplinary research team comprising doctors, clinicians and IT engineers. The paper shows exemplary results on the on-line evaluation of Iron Overload during the health status assessment and care management of β-Thalassemia patients.