Introduction:Assisted reproductive technologies (ART) are recent improvements in infertility treatment. However, there is no significant increase in pregnancy rates with the aid of ART. Costly and complex process of ART’s makes them as challenging issues. Computational prediction models could predict treatment outcome, before the start of an ART cycle.Aim:This review provides an overview on machine learning–based prediction models in ART.Methods:This article was executed based on a literature review through scientific databases search such as PubMed, Scopus, Web of Science and Google Scholar.Results:We identified 20 papers reporting on machine learning–based prediction models in IVF or ICSI settings. All of the models were validated only by internal validation. Therefore, external validation of the models and the impact analysis of them were the missing parts of the all studies.Conclusion:Machine learning–based prediction models provide a clinical decision support tool for both clinicians and patients and lead to improvement in ART success rates.

The aim of this study is to develop a computational prediction model for implantation outcome after an embryo transfer cycle. In this study, information of 500 patients and 1360 transferred embryos, including cleavage and blastocyst stages and fresh or frozen embryos, from April 2016 to February 2018, were collected. The dataset containing 82 attributes and a target label (indicating positive and negative implantation outcomes) was constructed. Six dominant machine learning approaches were examined based on their performance to predict embryo transfer outcomes. Also, feature selection procedures were used to identify effective predictive factors and recruited to determine the optimum number of features based on classifiers performance. The results revealed that random forest was the best classifier (accuracy = 90.40% and area under the curve = 93.74%) with optimum features based on a 10-fold cross-validation test. According to the Support Vector Machine-Feature Selection algorithm, the ideal numbers of features are 78. Follicle stimulating hormone/human menopausal gonadotropin dosage for ovarian stimulation was the most important predictive factor across all examined embryo transfer features. The proposed machine learning-based prediction model could predict embryo transfer outcome and implantation of embryos with high accuracy, before the start of an embryo transfer cycle.

Background: Gaining health is an inalienable right of every human being; therefore, governments are required to provide a minimum of health care services for all people who live in the society. Objectives: This study was conducted to compare the health care system of Iran and some selected countries around the world. Methods: This was a descriptive-comparative study, which was conducted to compare the health care system of Iran and a number of selected countries with a focus on the service provider and payment method. In this research, nine countries including Norway, Australia, United States of America, Germany, Italy, Canada, England, Denmark and Japan were selected and studied based on the availability of data. These data were compared to that of Iran. The required information from selected countries was collected in 2014 using the "health system review: health system in transition", and "international profiles of health care systems", as well as well-known websites such as the world health organization, the world bank and the health department. Results: The findings of this study showed that in most selected countries, primary care services were provided by the private sector and the public sector has been mostly functioning as a supervisor in this area, but in Iran, primary care services were provided by the government. The findings of this study also showed that hospital services in Iran and selected countries (second and third level services) were provided by both public and private sectors, yet the public sector had a bigger share. Moreover, payment in primary health care (PHC) in the majority of the selected countries was mostly capitation and FFS payments, or a combination of the two. Payment in hospital care (secondary and tertiary levels) in most of the studied countries and even Iran was mostly through governmental budgets. Conclusions: According to the findings of this comparative study indicating the successful experiences of health systems around the world, it seems that the implementation of the process of decentralization of the government in some sections and different levels of health care is the best option for the health care system of Iran.

Background and aimsBusiness-IT Alignment Evaluation is One of the most important issues that managers should monitor and make decisions about it. Dashboard software combines data and graphical indicators to deliver at-a-glance summaries of information for users to view the state of their business and quickly respond. The aim of this study was to design a dashboard to assess the business-IT alignment strategies for hospitals organizations in Tehran University of Medical Sciences.MethodsThis is a functional-developmental study. Initially, we searched related databases (PubMed and ProQuest) to determine the key performance indicators of business-IT alignment for selecting the best model for dashboard designing. After selecting the Luftman model, the key indicators were extracted for designing the dashboard model. In the next stage, an electronic questionnaire was designed based on extracted indicators. This questionnaire sends to Hospital managers and IT administrators. Collected data were analyzed by Excel 2015 and displayed in dashboard page.ResultsThe number of key performance indicators was 39. After recognition the technical requirements the dashboard was designed in Excel. The overall business-IT alignment rate in hospitals was 3.12. Amir-aalam hospital has the highest business-IT alignment rate (3.55) and vali-asr hospital has the lowest business-IT alignment rat (2.80).ConclusionUsing dashboard software improves the alignment and reduces the time and energy compared with doing this process manually.