There are a great number of uncertainties in medical practice, causing considerable difficulties in medical activities such as diagnosis and prognostic prediction. Neural-fuzzy system (NFS) combines the advantages of artificial neural networks and fuzzy logic very well, and has become a new type of artificial intelligence model which is capable of acquiring knowledge from data and expressing it in the form of fuzzy rules. Because of its strong capability of classification and processing fuzzy information, NFS is more and more used in medical practice. Adaptive neural-fuzzy inference system (ANFIS) is one of the most popular forms of NFS. This review focuses on the use of ANFIS in medical practice.
ObjectiveTo summarize and explore the clinical management and practical effect of ambulatory hysteroscopic surgery.MethodsThe annual operation volume, disease types, three- and four-grade operation proportion, complications, hospitalization expenses, patient satisfaction and average length of hospital stay of patients undergoing ambulatory hysteroscopic surgery in the First Affiliated Hospital of Chongqing Medical University between September 2014 and August 2019 were retrospectively analyzed.ResultsA total of 5 446 patients underwent hysteroscopic surgery during the five-year period, of whom 569 cases underwent the operation in conventional hospitalization mode in the first year and 4 877 cases did in ambulatory mode in the following four years. The quantity of hysteroscopy operations increased by stages (P<0.001) and the structure of disease types was optimized. The proportion of three- and four-grade complex operations increased from 48.15% to 79.15% stage by stage in ambulatory mode (P<0.05); ambulatory hysteroscopic surgery was proved to be safe and controllable with a low incidence of complications [0.43% (21/4 877)]; the average hospitalization cost of the patients was significantly lower than that of the conventional hospitalization mode (P<0.05); the score of patient satisfaction increased from 92.90±1.77 to 94.57±2.11 compared with the conventional hospitalization mode, and increased further with the construction of the specialized platform to 96.19±2.24 (P<0.05); the average length of hospital stay in ambulatory mode was significantly shortened than that in conventional hospitalization mode (P<0.05).ConclusionHysteroscopic surgery in ambulatory mode can improve the efficiency of medical services, ensure patient safety, improve patient satisfaction, reduce the average length of hospital stay, and reduce the economic burden of patients, so it could be widely promoted and standardized in clinical practice.
Abstract： Diseases prognosis is often influenced by multiple factors, and some intricate non-linear relationships exist among those factors. Artificial neural network （ANN）, an artificial intelligence model, simulates the work mode of biological neurons and has a b capability to analyze multi-factor non-linear relationships. In recent years, ANN is increasingly applied in clinical medical fields, especially for the prediction of disease prognosis. This article focuses on the basic principles of ANN and its application in disease prognosis research.