Expert Systems with Applications: An International Journal
An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM
Expert Systems with Applications: An International Journal
IEEE Transactions on Knowledge and Data Engineering
On aggregating uncertain information by type-2 OWA operators for soft decision making
International Journal of Intelligent Systems
Learning patient-specific predictive models from clinical data
Journal of Biomedical Informatics
IEEE Transactions on Knowledge and Data Engineering
Fuzzy inference neural network for fuzzy model tuning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Application of simulated annealing fuzzy model tuning to umbilical cord acid-base interpretation
IEEE Transactions on Fuzzy Systems
Uncertain Fuzzy Reasoning: A Case Study in Modelling Expert Decision Making
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Fuzzy logic for decision support in chronic care
Artificial Intelligence in Medicine
Interval type-2 fuzzy logic for encoding clinical practice guidelines
Knowledge-Based Systems
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It has been often demonstrated that clinicians exhibit both inter-expert and intra-expert variability when making difficult decisions. In contrast, the vast majority of computerized models that aim to provide automated support for such decisions do not explicitly recognize or replicate this variability. Furthermore, the perfect consistency of computerized models is often presented as a de facto benefit. In this paper, we describe a novel approach to incorporate variability within a fuzzy inference system using non-stationary fuzzy sets in order to replicate human variability. We apply our approach to a decision problem concerning the recommendation of post-operative breast cancer treatment; specifically, whether or not to administer chemotherapy based on assessment of five clinical variables: NPI (the Nottingham Prognostic Index), estrogen receptor status, vascular invasion, age and lymph node status. In doing so, we explore whether such explicit modeling of variability provides any performance advantage over a more conventional fuzzy approach, when tested on a set of 1310 unselected cases collected over a fourteen year period at the Nottingham University Hospitals NHS Trust, UK. The experimental results show that the standard fuzzy inference system (that does not model variability) achieves overall agreement to clinical practice around 84.6% (95% CI: 84.1-84.9%), while the non-stationary fuzzy model can significantly increase performance to around 88.1% (95% CI: 88.0-88.2%), p