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Background: This study was conducted to classify the types of evaluation methods in clinical health technologies based on a systematic review method. Methods: An electronic search was conducted in three scientific databases including Scopus, PubMed and ISI. The search strategy was performed in Jul to Nov 2021 and based on the three main concepts of "evaluation", "technology", "health. This search has been restricted to 10 years (2011-2021). Moreover, it only was limited to English and papers published in journals and conferences proceeding. Results: Overall, 8149 references were identified for title and abstract screening. Full text screening was performed for 2674 articles, with 174 meeting the criteria for study inclusion. Conclusion: Most of the technologies evaluated in these articles were associated with PC-based systems (N=107), and there have been fewer mobile apps (N=67). Most of used technologies were with goals of treatment (43%, N=74) and education (26%, N=45). Among all the methods, the most and the least used methods were usability (66%, N=115) and qualitative (1%, N=2) method, respectively. The most method for health clinical technologies is usability method especially in telemedicine field.

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Currently, the novel COVID-19 coronavirus has been widely spread as a global pandemic. The COVID-19 pandemic has a major influence on human life, healthcare systems, and the economy. There are a large number of methods available for predicting the incidence of the virus. A complex and non-stationary problem such as the COVID-19 pandemic is characterized by high levels of uncertainty in its behavior during the pandemic time. The fuzzy logic, especially Type-2 Fuzzy Logic, is a robust and capable model to cope with high-order uncertainties associated with non-stationary time-dependent features. The main objective of the current study is to present a novel Deep Interval Type-2 Fuzzy LSTM (DIT2FLSTM) model for prediction of the COVID-19 incidence, including new cases, recovery cases, and mortality rate in both short and long time series. The proposed model was evaluated on real datasets produced by the world health organization (WHO) on top highly risked countries, including the USA, Brazil, Russia, India, Peru, Spain, Italy, Iran, Germany, and the U.K. The results confirm the superiority of the DIT2FLSTM model with an average area under the ROC curve (AUC) of 96% and a 95% confidence interval of [92-97] % in the short-term and long-term. The DIT2FLSTM was applied to a well-known standard benchmark, the Mackey-Glass time-series, to show the robustness and proficiency of the proposed model in uncertain and chaotic time series problems. The results were evaluated using a 10-fold cross-validation technique and statistically validated through the t-test method. The proposed DIT2FLSTM model is promising for the prediction of complex problems such as the COVID-19 pandemic and making strategic prevention decisions to save more lives.

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Fuzzy inference systems have been frequently used in medical diagnosis for managing uncertainty sources in the medical images. In addition, fuzzy systems have high level of interpretability because of using linguistic terms for knowledge representation through the reasoning process. The evolutionary algorithms can be applied for optimization of the systems. This article takes advantages of differential evolutionary algorithm to search the problem space more intelligently using the knowledge of distance vector of the candidate solutions. For this, a hybrid fuzzy-DE model has been proposed for the problem of classification of the Haptic metastasis tumors through information obtained from the features measured in the CT scan images by radiologists. The hybrid fuzzy-DE model was evaluated using a real liver cancer dataset obtained from the Noor medical imaging center in Tehran. The results of the hybrid proposed models were compared with the diagnosis of the specialists, the results reveal that the proposed fuzzy-DE model has high capability for diagnosis of the hepatic metastasis tumors with an accuracy of 99.24% with 95% confidence interval (98.32 100) in terms of area under the ROC curve. The proposed model outperforms the multilayer perceptron (MLP) neural network and fuzzy-genetic algorithm (GA). The proposed model improves the trade-offs between the accuracy and interpretability by providing a model with high accuracy using less input variables. The hybrid fuzzy-DE model is promising to assist the medical specialists for early diagnosis of liver cancer and save more people lives.

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