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With the rapid growth of technology, computer learning has become increasingly integrated with artificial intelligence techniques in order to develop more personalized educational systems. These systems are known as Intelligent Tutoring systems (ITSs). This paper focused on the variant characteristics of ITSs developed across different educational fields. The original studies from 2007 to 2017 were extracted from the PubMed, ProQuest, Scopus, Google scholar, Embase, Cochrane, and Web of Science databases. Finally, 53 papers were included in the study based on inclusion criteria. The educational fields in the ITSs were mainly computer sciences (37.73%). Action-condition rule-based reasoning, data mining, and Bayesian network with 33.96%, 22.64%, and 20.75% frequency respectively, were the most frequent artificial intelligent techniques applied in the ITSs. These techniques enable ITSs to deliver adaptive guidance and instruction, evaluate learners, define and update the learner's model, and classify or cluster learners. Specifically, the performance of the system, learner's performance, and experiences were used for evaluation of ITSs. Most ITSs were designed for web user interfaces. Although these systems could facilitate reasoning in the learning process, these systems have rarely been applied in experimental courses including problem-solving, decision-making in physics, chemistry, and clinical fields. Due to the important role of a cell phone in facilitating personalized learning and given the low rate of using mobile-based ITSs, this study has recommended the development and evaluation of mobile-based ITSs.

Introduction: Chronic hepatitis B (CHB) leads to liver fibrosis, its failure, and death in the long term. The stage of fibrosis in CHB patients can also be detected based on the biochemical markers. The aim of this study was to predict the state of liver fibrosis in CHB patients and determine the possibility of patients shifting from a given state to another one. Materials and Methods: This study is a cross-sectional study conducted in 2021. Age, blood platelet count, AST, and ALT enzymes were used as the input variable to create predictive models. Predictive models were Decision Tree (DT), Naïve Bayes, Support Vector Machine (SVM), and Neural Network (NN). The probability of a patient shifting from a given stage of fibrosis to another was calculated using the transition matrix. The 10-fold cross-validation was used to ensure the generalization of predictive models. Results: The DT had the best precision, recall, and accuracy (100%) among developed algorithms to predict the stage of fibrosis in CHB patients. The NN was the second most efficient algorithm. Its accuracy and mean square error was 99.35±0.60 and 0.058±0.025, respectively. Besides, SVM had the lowest recall, precision, and accuracy values. Based on the transition matrix results, there is a very low probability that the patients with non-significant fibrosis state shifted to the cirrhosis state. Conclusion: Computational approaches like machine learning algorithms are the non-invasive way to predict the fibrosis state in CHB patients efficiently. [ABSTRACT FROM AUTHOR]

Background: Timely rehabilitation in patients with Breast Cancer (BC) has a great impact on improving their physical and mental conditions. Thus, the appropriate follow-up method is essential especially during the Covid-19 pandemic. The aim of this study was to review the different technology-assisted interventions for improving physical activity in BC patients. Materials And Methods: In this systematic review, the original studies were extracted from the beginning of indexing in databases including PubMed, Scopus, Google Scholar, and Web of Science until 2019. Finally, 45 papers were included in this study based on the inclusion criteria for before the Covid-19 pandemic and 3 articles extracted for the Covid-19 period. Results: The most widely used technologies for BC patients were in the United States (46.67%). Telephone, mobile application, and web with 28.89%, 15.56%, and 8.89% frequencies were the most common technologies, respectively. Although the majority of the participants were satisfied with the intervention method, in some cases, the patients were unsatisfied due to the complexity of the technology. These technologies were used for various purposes, such as physical activity and functions, control of pain severity, fitness, quality of life, diet behavior, fatigue, muscle strength, cardio-respiratory capacity, as well as arm and shoulder exercises. Conclusions: In conclusion, virtual communication can improve the health of BC patients and also increases patients' desire and hope to continue treatment. It is worth noting that in the Covid-19 pandemic, with the strengthening of virtual communication infrastructure, more attention was paid to BC patients due to their sensitive conditions. [ABSTRACT FROM AUTHOR]

Background: In developing countries such as Iran, international standards offer good sources to survey and use for appropriate planning in the domain of electronic health records (EHRs). Therefore, in this study, HL7 and ASTM standards were considered as the main sources from which to extract EHR data. Objective: The objective of this study was to propose a hospital data set for a national EHR consisting of data classes and data elements by adjusting data sets extracted from the standards and paperbased records. Method: This comparative study was carried out in 2017 by studying the contents of the paper-based records approved by the health ministry in Iran and the international ASTM and HL7 standards in order to extract a minimum hospital data set for a national EHR. Results: As a result of studying the standards and paper-based records, a total of 526 data elements in 174 classes were extracted. An examination of the data indicated that the highest number of extracted data came from the free text elements, both in the paper-based records and in the standards related to the administrative data. The major sources of data extracted from ASTM and HL7 were the E1384 and Hl7V.x standards, respectively. In the paper-based records, data were extracted from 19 forms sporadically. Discussion: By declaring the confidentiality of information, the ASTM standards acknowledge the issue of confidentiality of information as one of the main challenges of EHR development, and propose new types of admission, such as teleconference, tele-video, and home visit, which are inevitable with the advent of new technology for providing healthcare and treating diseases. Data related to finance and insurance, which were scattered in different categories by three organizations, emerged as the financial category. Documenting the role and responsibility of the provider by adding the authenticator/signature data element was deemed essential. Conclusion: Not only using well-defined and standardized data, but also adapting EHR systems to the local facilities and the existing social and cultural conditions, will facilitate the development of structured data sets. [ABSTRACT FROM AUTHOR]

Background: The present study aim is to predict the psychological inclination to drug use in youths by studying their attachment styles. Materials and Methods: The research sample includes male and female students of Zahedan Medical Science University with the average age of 19-24. The proportional cluster random sampling was used for selection of participant. The hypotheses were analyzed, using Pearson correlation method, regression analysis, one way variance analysis and t-test for two independent groups4T. Results: The results indicated positive relationships among addiction aptitude and insecure-avoidant attachment style and negative relationship between addiction aptitude and secure attachment style4T. Conclusion: It is necessary to focus training intervention and prevention on all students4T.4T [ABSTRACT FROM AUTHOR]

Competing Interests: Conflicts of interest The authors declare that there is no conflict of interests.