ABSTRACT
INTRODUCTION: Iron deficiency anemia (IDA) and ß-thalassemia trait (ß-TT) are the most common types of microcytic hypochromic anemias. The similarity and the nature of anemia-related symptoms pose a foremost challenge for discriminating between IDA and ß-TT. Currently, advances in technology have gave rise to computer-based decision-making systems. Therefore, advances in artificial intelligence have led to the emergence of intelligent systems and the development of tools that can assist physicians in the diagnosis and decision-making. AIM: The aim of the present study was to develop a neural network based model (Artificial Neural Network) for accurate and timely manner of differential diagnosis of IDA and ß-TT in comparison with traditional methods. METHODS: In this study, an artificial neural network (ANN) model as the first precise intelligent method was developed for differential diagnosis of IDA and ß-TT. Data set was retrieved from Complete Blood Count (CBC) test factors of 268 individuals referred to Padad private clinical laboratory at Ahvaz, Iran in 2018. ANN models with different topologies were developed and CBC indices were examined for diagnosis of IDA and ß-TT. The proposed model was simulated using MATLAB software package version 2018. The results showed the best network architecture based on the advanced multilayer algorithm (4 input factors, 70 neurons with acceptable sensitivity, specificity, and accuracy). Finally, the results obtained from ANN diagnostic model was compared to existing discriminating indexes. RESULT: The results of this model showed that the specificity, sensitivity, and accuracy of the proposed diagnostic system were 92.33%, 93.13%, and 92.5%, respectably; i.e. the model could diagnose frequent occurrence of IDA in patients with ß-TT. CONCLUSION: The results and evaluation of the developed model showed that the proposed neural network model has a proper accuracy and generalizability based on the initial factors of CBC testing compared to existing methods. This model can replace the high-cost methods and discriminating indices to distinguish IDA from ß-TT and assist in accurate and timely manner diagnosis.
ABSTRACT
INTRODUCTION: High blood pressure or hypertension is one of the chronic diseases causing other serious diseases and syndromes. Active involvement of the patient in the management of the disease is crucial in improving self-care and clinical outcomes. Mobile technology is nowadays used widely to improve the self-care process in people with chronic diseases such as hypertension. AIM: The objective of this study was to provide an overview of the existing research evaluating the impact of mobile applications on the self-care of patients with hypertension. METHODS: The Scopus and PubMed databases were investigated using a comprehensive search strategy from the beginning of 2010 to 2019. All controlled clinical trial studies as well as quasi-experimental studies used mobile as a device for improving the self-care and conducted on patients with hypertension were included in the study. The studies were reviewed by two independent individuals. RESULTS: Out of 1032 studies found, 6 studies were finally reviewed after applying the inclusion criteria. Out of 6 studies reviewed, three studies confirmed the effect of using mobile applications on lowering blood pressure. Other studies reported a decline in blood pressure, while statistically significant were not shown. CONCLUSION: The results showed that mobile apps have positive potential on improving the self-care behavior of patients with hypertension, but the evidences presenting their impact are varied. Different reports for efficiency of mobile phone apps for the self-care modification was due to diverse condition of studies for mobile intervention on the patients with hypertension.
