Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Using fuzzy numbers to evaluate perceived service quality
Fuzzy Sets and Systems - Special issue on fuzzy numbers and uncertainty
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Adaptive ventilator Fi02 advisor: use of non-invasive estimations of shunt
Artificial Intelligence in Medicine
Journal of Medical Systems
Factors Affecting Inpatient Satisfaction: Structural Equation Modeling
Journal of Medical Systems
Artificial Intelligence in Medicine
Methods to Evaluate Health information Systems in Healthcare Settings: A Literature Review
Journal of Medical Systems
A fuzzy model of customer satisfaction index in e-commerce
Mathematics and Computers in Simulation
Fuzzy neural based importance-performance analysis for determining critical service attributes
Expert Systems with Applications: An International Journal
Surveying stock market forecasting techniques - Part II: Soft computing methods
Expert Systems with Applications: An International Journal
Applied Soft Computing
Expert Systems with Applications: An International Journal
A neuro-fuzzy approach for prediction of human work efficiency in noisy environment
Applied Soft Computing
An optimized experimental protocol based on neuro-evolutionary algorithms
Artificial Intelligence in Medicine
Computers in Biology and Medicine
Review: Development of soft computing and applications in agricultural and biological engineering
Computers and Electronics in Agriculture
Artificial Intelligence in Medicine
On the use and usefulness of fuzzy sets in medical AI
Artificial Intelligence in Medicine
A survey of fuzzy logic monitoring and control utilisation in medicine
Artificial Intelligence in Medicine
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Objectives: To develop and explore the predictability of patient perceptions of satisfaction through the hospital adoption of health information technology (HIT), leading to a better understanding of the benefits of increased HIT investment. Data and methods: The solution proposed is based on comparing the predictive capability of artificial neural networks (ANNs) with the adaptive neuro-fuzzy inference system (ANFIS). The latter integrates artificial neural networks and fuzzy logic and can handle certain complex problems that include fuzziness in human perception, and non-normal and non-linear data. Secondary data from two surveys were combined to develop the model. Hospital HIT adoption capability and use indicators in the Canadian province of Ontario were used as inputs, while patient satisfaction indicators of healthcare services in acute hospitals were used as outputs. Results: Eight different types of models were trained and tested for each of four patient satisfaction dimensions. The accuracy of each predictive model was evaluated through statistical performance measures, including root mean square error (RMSE), and adjusted coefficient of determination R^2"A"d"j"u"s"t"e"d. For all four patient satisfaction indicators, the performance of ANFIS was found to be more effective (R"A"d"j"u"s"t"e"d^2=0.99) when compared with the results of ANN modeling in predicting the impact of HIT adoption on patient satisfaction (R"A"d"j"u"s"t"e"d^2=0.86-0.88). Conclusions: The impact of HIT adoption on patient satisfaction was obtained for different HIT adoption scenarios using ANFIS simulations. The results through simulation scenarios revealed that full implementation of HIT in hospitals can lead to significant improvement in patient satisfaction. We conclude that the proposed ANFIS modeling technique can be used as a decision support mechanism to assist government and policy makers in predicting patient satisfaction resulting from the implementation of HIT in hospitals