Using a Fuzzy Neural Network in Clinical Decision Support for Patients with Advanced Heart Failure Determining the appropriate timing of mechanical circulatory support (MCS) or heart transplantation (HT) for patients with advanced heart failure is essential as there may be a mortality cost to delayed care. An automated decisionmaking system that can identify patients eligible for a HTMCS would facilitate primary care physicians or general cardiologists referring those patients for consideration of advanced therapies. In this study, a novel fuzzy neural network was built by integrating fuzzy set theory, neural network, and genetic algorithm techniques. The overall architecture of the proposed fuzzy neural network was inspired by clinical practice guidelines. Clinical variables were encoded using fuzzy concepts and rules were calculated in a fullyconnected layer with constraints in weights. From the experiments, the proposed fuzzy neural network achieved an average AUC of 0.838 and an F1 score of 0.462. The rules from the trained network were further analyzed. Our results show that the proposed fuzzy neural network can not only achieve good classification performance, but also provides transparency with respect to knowledge extraction and interpretation.

Normative and Fuzzy Aspects of Medical AI Normative and fuzzy aspects of Medical AI are examined and a framework is presented for the purpose of designing architectures that can accommodate the lifelong growth of data and knowledge pertaining to an individualu0027s health, together with relevant privileges and permissions for access, growth and utilization of such a system. Considering disease through the normativistic perspective advanced by SadeghZadeh, an actiontheoretical analysis is adopted suitable for Medical AI. In this approach diseases are viewed as social artifacts mapped into deontic constructs. In a sense, they are action provoking states of affairs amenable to fuzzy computational approaches that in the past have found broad applicability to industrial process control.