Use of sentiment analysis for capturing hospitalized cancer patients' experience from free-text comments in the Persian language.

PURPOSE: Today, the Internet provides access to many patients' experiences, which is crucial in assessing the quality of healthcare services. This paper introduces a model for detecting cancer patients' opinions about healthcare services in the Persian language, both positive and negative. METHOD: To achieve the objectives of this study, a combination of sentiment analysis (SA) and topic modeling approaches was employed. All pertinent comments made by cancer patients were collected from the patient feedback form of the Tehran University of Medical Science (TUMS) Cancer Institute (CI) in Iran, from March to October 2021. Conventional evaluation metrics such as accuracy, precision, recall, and F-measure were utilized to assess the performance of the proposed model. RESULT: The experimental findings revealed that the proposed SA model achieved accuracies of 89.3%, 92.6%, and 90.8% in detecting patients' sentiments towards general services, healthcare services, and life expectancy, respectively. Based on the topic modeling results, the topic "Metastasis" exhibited lower sentiment scores compared to other topics. Additionally, cancer patients expressed dissatisfaction with the current appointment booking service, while topics such as "Good experience," "Affable staff", and "Chemotherapy" garnered higher sentiment scores. CONCLUSION: The combined use of SA and topic modeling offers valuable insights into healthcare services. Policymakers can utilize the knowledge obtained from these topics and associated sentiments to enhance patient satisfaction with cancer institution services.

Using an adaptive network-based fuzzy inference system for prediction of successful aging: a comparison with common machine learning algorithms.

INTRODUCTION: The global society is currently facing a rise in the elderly population. The concept of successful aging (SA) appeared in the gerontological literature to overcome the challenges and problems of population aging. SA is a subjective and multidimensional concept with many ambiguities regarding its meaning or measuring. This study aimed to propose an intelligent predictive model to predict SA. METHODS: In this retrospective study, the data of 784 elderly people were used to develop and validate machine learning (ML) methods. Data pre-processing was first performed. First, an adaptive neuro-fuzzy inference system (ANFIS) was proposed to predict SA. Then, the predictive performance of the proposed model was compared with three ML algorithms, including multilayer perceptron (MLP) neural network, support vector machine (SVM), and random forest (RF) based on accuracy, sensitivity, precision, and F-score metrics. RESULTS: The findings indicated that the ANFIS model with gauss2mf built-in membership function (MF) outperformed the other models with accuracy, sensitivity, precision, and F-score of 91.57%, 95.18%, 92.31%, and 92.94%, respectively. CONCLUSIONS: The predictive performance of ANFIS is more efficient than the other ML models in SA prediction. The development of a decision support system (DSS) using our prediction model can provide healthcare administrators and policymakers with a reliable and responsive tool to improve elderly outcomes.

Investigating the performance of machine learning algorithms in predicting the survival of COVID-19 patients: A cross section study of Iran.

Background and Aims: Like early diagnosis, predicting the survival of patients with Coronavirus Disease 2019 (COVID-19) is of great importance. Survival prediction models help doctors be more cautious to treat the patients who are at high risk of dying because of medical conditions. This study aims to predict the survival of hospitalized patients with COVID-19 by comparing the accuracy of machine learning (ML) models. Methods: It is a cross-sectional study which was performed in 2022 in Fasa city in Iran country. The research data set was extracted from the period February 18, 2020 to February 10, 2021, and contains 2442 hospitalized patients' records with 84 features. A comparison was made between the efficiency of five ML algorithms to predict survival, includes Naive Bayes (NB), K-nearest neighbors (KNN), random forest (RF), decision tree (DT), and multilayer perceptron (MLP). Modeling steps were done with Python language in the Anaconda Navigator 3 environment. Results: Our findings show that NB algorithm had better performance than others with accuracy, precision, recall, F-score, and area under receiver operating characteristic curve of 97%, 96%, 96%, 96%, and 97%, respectively. Based on the analysis of factors affecting survival, heart disease, pulmonary diseases and blood related disease were the most important disease related to death. Conclusion: The development of software systems based on NB will be effective to predict the survival of COVID-19 patients.

A fog-assisted information model based on priority queue and clinical decision support systems.

OBJECTIVES: Telehealth monitoring applications are latency-sensitive. The current fog-based telehealth monitoring models are mainly focused on the role of the fog computing in improving response time and latency. In this paper, we have introduced a new service called "priority queue" in fog layer, which is programmed to prioritize the events sent by different sources in different environments to assist the cloud layer with reducing response time and latency. MATERIAL AND METHODS: We analyzed the performance of the proposed model in a fog-enabled cloud environment with the IFogSim toolkit. To provide a comparison of cloud and fog computing environments, three parameters namely response time, latency, and network usage were used. We used the Pima Indian diabetes dataset to evaluate the model. RESULT: The fog layer proved to be very effective in improving the response time while handling emergencies using priority queues. The proposed model reduces response time by 25.8%, latency by 36.18%, bandwidth by 28.17%, and network usage time by 41.4% as compared to the cloud. CONCLUSION: By combining priority queues, and fog computing in this study, the network usage, latency time, bandwidth, and response time were significantly reduced as compared to cloud computing.

Technical aspects of virtual augmented reality-based rehabilitation systems for musculoskeletal disorders of the lower limbs: a systematic review.

INTRODUCTION: Musculoskeletal disorders are one of the most common causes of physical disability. The rehabilitation process after musculoskeletal disorders is long and tedious, and patients are not motivated to follow rehabilitation protocols. Therefore, new systems must be used to increase patient motivation. Virtual reality (VR) and augmented reality (AR) technologies can be used in this regard. In developing such systems, various technologies and methods of movement recognition are used; therefore, this study aims to summarize the technical aspects of using VR/AR in rehabilitation and evaluate and discuss efficient methods of investigating studies using the Statement of Standards for Reporting Implementation Studies (StaRI). METHODS: Search in four scientific databases was done systematically based on PRISMA through online search engines from inception to June 2021. These databases include Medline (PubMed), Scopus, IEEE, and Web of Science. An updated search was also conducted on 17 December 2021. The research used keywords and MeSH terms associated with VR/AR, musculoskeletal disorder, and rehabilitation. Selected articles were evaluated qualitatively using the Standards for Reporting Implementation Studies (StaRI) statement. RESULTS: A total of 2343 articles were found, and 20 studies were included. We found that 11 (55%) studies used Kinect technology as input tools, and 15 (75%) studies have described the techniques used to analyze human movements, such as dynamic time warping (DTW) and support vector machines (SVM). In 10 (50%) studies, the Unity game engine was used for visualization. In 8 studies (40%), usability was assessed, and high usability was reported. Similarly, the results of the review of studies according to the StaRI checklist showed poor reporting in the title and discussion of the studies. CONCLUSIONS: We found that academic studies did not describe the technical aspects of rehabilitation systems. Therefore, a good description of the technical aspects of the system in such studies should be considered to provide repeatability and generalizability of these systems for investigations by other researchers.

Diagnosis of COVID-19 Disease in Chest CT-Scan Images Based on Combination of Low-Level Texture Analysis and MobileNetV2 Features.

Since two years ago, the COVID-19 virus has spread strongly in the world and has killed more than 6 million people directly and has affected the lives of more than 500 million people. Early diagnosis of the virus can help to break the chain of transmission and reduce the death rate. In most cases, the virus spreads in the infected person's chest. Therefore, the analysis of a chest CT scan is one of the most efficient methods for diagnosing a patient. Until now, various methods have been presented to diagnose COVID-19 disease in chest CT-scan images. Most recent studies have proposed deep learning-based methods. But handcrafted features provide acceptable results in some studies too. In this paper, an innovative approach is proposed based on the combination of low-level and deep features. First of all, local neighborhood difference patterns are performed to extract handcrafted texture features. Next, deep features are extracted using MobileNetV2. Finally, a two-level decision-making algorithm is performed to improve the detection rate especially when the proposed decisions based on the two different feature set are not the same. The proposed approach is evaluated on a collected dataset of chest CT scan images from June 1, 2021, to December 20, 2021, of 238 cases in two groups of patient and healthy in different COVID-19 variants. The results show that the combination of texture and deep features can provide better performance than using each feature set separately. Results demonstrate that the proposed approach provides higher accuracy in comparison with some state-of-the-art methods in this scope.

A priority queue-based telemonitoring system for automatic diagnosis of heart diseases in integrated fog computing environments.

Various studies have shown the benefits of using distributed fog computing for healthcare systems. The new pattern of fog and edge computing reduces latency for data processing compared to cloud computing. Nevertheless, the proposed fog models still have many limitations in improving system performance and patients' response time.This paper, proposes a new performance model by integrating fog computing, priority queues and certainty theory into the Edge computing devices and validating it by analyzing heart disease patients' conditions in clinical decision support systems (CDSS). In this model, a Certainty Factor (CF) value is assigned to each symptom of heart disease. When one or more symptoms show an abnormal value, the patient's condition will be evaluated using CF values in the fog layer. In the fog layer, requests are categorized in different priority queues before arriving into the system. The results demonstrate that network usage, latency, and response time of patients' requests are respectively improved by 25.55%, 42.92%, and 34.28% compared to the cloud model. Prioritizing patient requests with respect to CF values in the CDSS provides higher system Quality of Service (QoS) and patients' response time.

Supervised Machine Learning Approach to COVID-19 Detection Based on Clinical Data.

Background: The new coronavirus has been spreading since the beginning of 2020, and many efforts have been made to develop vaccines to help patients recover. It is now clear that the world needs a rapid solution to curb the spread of COVID-19 worldwide with non-clinical approaches such as artificial intelligence techniques. These approaches can be effective in reducing the burden on the health care system to provide the best possible way to diagnose the COVID-19 epidemic. This study was conducted to use Machine Learning (ML) algorithms for the early detection of COVID-19 in patients. Methods: This retrospective study used data from hospitals affiliated with Shiraz University of Medical Sciences in Iran. This dataset was collected in the period March to October 2020 andcontained 10055 cases with 63 features. We selected and compared six algorithms: C4.5, support vector machine (SVM), Naive Bayes, logistic Regression (LR), Random Forest, and K-Nearest Neighbor algorithm using Rapid Miner software. The performance of algorithms was measured using evaluation metrics, such as precision, recall, accuracy, and f-measure. Results: The results of the study show that among the various used classification methods in the diagnosis of coronavirus, SVM (93.41% accuracy) and C4.5 (91.87% accuracy) achieved the highest performance. According to the C4.5 decision tree, "contact with a person who has COVID-19" was considered the most important diagnostic criterion based on the Gini index. Conclusion: We found that ML approaches enable a reasonable level of accuracy in the diagnosis of COVID-19.

Development, usability and quality evaluation of the resilient mobile application for women with breast cancer.

Introduction: Mental health problems as a consequence of cancer lower the quality of life of cancer patients. Despite increasing studies of breast cancer-focused mobile health applications (m-Health apps), there is less research on breast cancer patients' quality of life or well-being. The purpose of this study is to develop and evaluate the usability and quality of an educational m-Health app aimed at improving the resilience of breast cancer in women. Methods: This study was conducted in four phases. It included extracting the requirements of the app through the nominal group technique. Based on these results, an m-Health app was developed and evaluated in terms of usability and quality by two scales, System Usability Scale and Mobile App Rating Scale questionnaires, respectively. Finally, the role of patients' age and educational backgrounds in the use of the app was assessed. The relationship between learnability and usability of the app was measured by the T-Test. Results: The app was developed with three user interfaces. Its usability developed from the patient's point of view scored a remarkable score of 83.20 with a 95% confidence interval. This value was too indicative of high satisfaction with the usefulness and the possibility of recommending it to other cancer survivors. The results of the quality evaluation from an expert's point of view showed that this app had good functionality. Evaluation of the role of demographic information in the use of the app showed that it can be used for all age groups with different levels of education. The app did not differ significantly between learnability and usability. Conclusion: The development of m-Health apps, based on usability principles that are suitable for all age groups with different levels of education, is welcomed by cancer patients.

Predictive modeling for COVID-19 readmission risk using machine learning algorithms.

INTRODUCTION: The COVID-19 pandemic overwhelmed healthcare systems with severe shortages in hospital resources such as ICU beds, specialized doctors, and respiratory ventilators. In this situation, reducing COVID-19 readmissions could potentially maintain hospital capacity. By employing machine learning (ML), we can predict the likelihood of COVID-19 readmission risk, which can assist in the optimal allocation of restricted resources to seriously ill patients. METHODS: In this retrospective single-center study, the data of 1225 COVID-19 patients discharged between January 9, 2020, and October 20, 2021 were analyzed. First, the most important predictors were selected using the horse herd optimization algorithms. Then, three classical ML algorithms, including decision tree, support vector machine, and k-nearest neighbors, and a hybrid algorithm, namely water wave optimization (WWO) as a precise metaheuristic evolutionary algorithm combined with a neural network were used to construct predictive models for COVID-19 readmission. Finally, the performance of prediction models was measured, and the best-performing one was identified. RESULTS: The ML algorithms were trained using 17 validated features. Among the four selected ML algorithms, the WWO had the best average performance in tenfold cross-validation (accuracy: 0.9705, precision: 0.9729, recall: 0.9869, specificity: 0.9259, F-measure: 0.9795). CONCLUSIONS: Our findings show that the WWO algorithm predicts the risk of readmission of COVID-19 patients more accurately than other ML algorithms. The models developed herein can inform frontline clinicians and healthcare policymakers to manage and optimally allocate limited hospital resources to seriously ill COVID-19 patients.

Evaluation of m-Health-rehabilitation for respiratory disorders: A systematic review.

Background and Aims: Chronic respiratory diseases are prominent causes of morbidity worldwide that impose significant social and economic burdens on individuals and communities. Pulmonary rehabilitation is one of the main aspects of medical rehabilitation. Nowadays, mobile health apps deliver pulmonary rehabilitation support via smartphones. This article presents a systematic review of the literature on m-Health apps used in respiration disorders rehabilitation. Methods: A systematic search was performed on MEDLINE (through PubMed), Web of Science, and Scopus in May 2021 without any date limitation. This study was using a combination of keywords and MeSH terms associated with pulmonary rehabilitation. Relevant studies were selected by two independents and were categorized studies results. The inclusion criterion was m-Health apps for pulmonary rehabilitation and exclusion criteria mobile-based interventions, by voice call or short message service and cardiopulmonary articles. Results: Searching scientific databases yielded 161 relevant articles. Then, 27 articles were included in the study with a complete evaluation of the articles. Sixty percent of them were related to patients with chronic obstructive pulmonary disease (COPD). Rehabilitation aiming to improve the quality of life, promote self-management, encourage physical activity, and reduce the symptoms as the most common goals of pulmonary rehabilitation using m-Health apps; 89% of these studies showed that m-Health apps can be effective in improving pulmonary rehabilitation. In addition, 37% of studies reported high usability and acceptance. However, the results of some studies show that adherence to apps decreases in the long run. Conclusion: Our study shows that m-Health pulmonary rehabilitation apps are effective in improving the quality of life, self-management, and physical activity. According to the results, it seems that using the m-Health apps for pulmonary rehabilitation can be useful in the COVID-19 pandemic and help reduce respiratory disorders in patients with COVID-19 disease.

Reviewing and Content Analysis of Persian Language Mobile Health Apps for COVID-19 Management.

The present study aimed to systematically search in app stores and intended to carry out content analysis of free Persian mobile health apps in the management of COVID-19 and, ultimately determine the relationship between the popularity and quality of these apps. According to a researcher-made checklist including five axes of ease of use, privacy, data sharing, education, and monitoring, four app markets such as Myket, Bazzar, Google Play and App Store were searched from May 2021 up to now. The findings showed that all selected apps performed well in terms of ease of use and privacy but they needed to be improved in terms of education, monitoring, and data sharing. Also, there was no significant relationship between the popularity and quality of these apps. Owing to the high penetration rate of smartphones in Iran and the low popularity of COVID-19 apps, government, developers, and investors are required to improve the quality of apps and their marketing.

Students' Satisfaction and e-Learning Courses in Covid-19 Pandemic Era: A Case Study.

The present study is conducted to determine the status of e-learning, student satisfaction and the relationship between these two variables in Zahedan University of Medical Sciences (ZAUMS). According to a descriptive study, there was just a significant difference between the mean score of e-Learning experience and student satisfaction, and a positive correlation between the education level and student satisfaction. Also, there was a positive correlation between all variables of e-learning and student satisfaction The findings showed that the more capable learners were outcome of better educational content, stronger e-learning infrastructure, better support and assessment of e-learning quality, which, in turn, resulted in the greater the students' satisfaction. As a result, the experiences from the evaluation of e-learning in the Covid-19 pandemic period may be regarded a good guide in improving the course during the Covid-19 pandemic, and also it can be considered a key factor in providing educations in the post-Covid-19 period.

Effectiveness of mobile health-based self-management application for posttransplant cares: A systematic review.

BACKGROUND AND AIMS: Patients after transplantation need medical management for the rest of their lives, and self-management seems to lead to greater adherence to medical standards, improve early physical changes, and increase patient empowerment. The main objective of this article is to systematic review of the consideration to mobile health applications (m-Health apps) used in transplantation. METHODS: A systematic search was conducted MEDLINE (through PubMed), Web of Science, Scopus, and Science Direct from inception to November 2020. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement was used in this study. Comprehensive research was carried out using a combination of keywords and MeSH terms associated with m-Health, empowerment, self-management, and transplantation. Two independent reviewers screened titles and abstracts, assessed full-text articles, and extracted data from articles that met inclusion criteria. Eligible studies were original research articles that included posttransplant care and mobile phone-based applications to support self-management and self-care. Also, thesis, book chapters, letters to editors, short briefs, reports, technical reports, book reviews, systematic reviews, or meta-analysis were excluded. RESULTS: We divided all the reviewed articles into four categories, self-management (medication adherence, adherence to medical regimen, and remote monitoring), evaluation, interaction, and interface; 37.5% of the studies were focused on lung transplantation. In 56.25% of the studies, medication adherence was considered because one of the main reasons for the rejection and graft loss is stated medication nonadherence. Also, 62.5% of the studies demonstrated that the use of m-health improved medication adherence and self-management in transplantation. CONCLUSIONS: The use of m-Health apps interventions to self-management after transplantation has shown promising feasibility and acceptability, and there is modest evidence to support the efficacy of these interventions. We found that m-Health solutions can help the patient in self-management in many ways after transplantation.

Training pain management to nursing students: Designing, implementing, and evaluating a mobile-based application.

BACKGROUND: Pain is a common health issue and acute pain is the main problem for patients after surgery and injury. Inadequate and inappropriate management of pain is dangerous and costly for patients and leads to undesirable health effects. To overcome this problem, empowerment of the health-care team, especially nurses, is essential. Today, to improve the quality of health-care provision, various methods are used that e-learning is one of them. MATERIALS AND METHODS: Based on the studies on pain management, existing parameters were extracted, and according to them, the educational content of the software was approved by nursing professors and anesthesiologists. The Unified Modeling Language diagrams were designed to provide a better understanding of the entities and the order in which the software operates. The software was implemented in the google android studio environment using Photoshop and JQuery mobile. Finally, the software was evaluated by using Questionnaire for User Interface Satisfaction. The software was evaluated by experts and students in two stages. The first stage was evaluated by eight anesthesiologists and nursing professors, and the second stage was evaluated with the participation of 55 undergraduate students and 28 M.Sc. nursing students. RESULTS: The software was developed with two main modules of training and testing, and sections of the report, about us and exit, and four scenarios for the test section. In the initial evaluation of software by experts with an average of 91.85%, and in the second assessment of students, with a mean of 78.15%, application software was evaluated at a good level. CONCLUSIONS: In order to teach academic and practical (clinical) materials to students, the use of digital teaching aids and e-learning, along with traditional methods such as lectures, increases the students' eagerness, and motivation to learn more and thereby enhance the level of learning and improving the quality of education.

Prerequisites of Personal Health Record for Chronic Kidney Disease: A Scoping Review and Evaluation of the Content Validity.

BACKGROUND: It is obvious that the Personal Health Record (PHR) is a major cornerstone for "improving the self-management of patient". However, lack of an effective and comprehensive personal health record system prohibits the widespread use of PHRs. The aim of this study was to identify the core data sets and required functionalities for designing a PHRs for chronic kidney disease (CKD) management and assess their validity. METHODS: It was a study including two phases. In the initial phase, a scoping review was conducted with the aim of determination the core data sets and required functionalities for designing PHRs. Then in the second phase, the validity of data items and functionalities was determined by 25 multidisciplinary experts. RESULTS: 22 studies were eligible after screening 1335 titles and abstracts and reviewing 88 full texts. We determined 20 core data set and 8 required functionalities of PHRs. From the perspective of experts, 'health maintenance' and 'advance directives' were most often marked as useful but not essential, while 'test and examination', 'medication list' and 'diagnosis and comorbid conditions" were predominantly considered as essential by all experts (n=25,100%). CONCLUSION: This research is a step that we have taken to identify prerequisites that could be used for the design, development, and implementation of an effective and comprehensive electronic personal health record.

Mobile Health Technology and Healthcare Providers: Systemic Barriers to Adoption.

OBJECTIVES: Despite the growing use of mobile health (mHealth), certain barriers seem to be hindering the use of mHealth applications in healthcare. This article presents a systematic review of the literature on barriers associated with mHealth reported by healthcare professionals. METHODS: This systematic review was carried out to identify studies published from January 2015 to December 2019 by searching four electronic databases (PubMed/MEDLINE, Web of Science, Embase, and Google Scholar). Studies were included if they reported perceived barriers to the adoption of mHealth from healthcare providers' perspectives. Content analysis and categorization of barriers were performed based on a focus group discussion that explored researchers' knowledge and experiences. RESULTS: Among the 273 papers retrieved through the search strategy, 18 works were selected and 18 barriers were identified. The relevant barriers were categorized into three main groups: technical, individual, and healthcare system. Security and privacy concerns from the category of technical barriers, knowledge and limited literacy from the category of individual barriers, and economic and financial factors from the category of healthcare system barriers were chosen as three of the most important challenges related to the adoption of mHealth described in the included publications. CONCLUSIONS: mHealth adoption is a complex and multi-dimensional process that is widely implemented to increase access to healthcare services. However, it is influenced by various factors and barriers. Understanding the barriers to adoption of mHealth applications among providers, and engaging them in the adoption process will be important for the successful deployment of these applications.

The use of mobile health interventions for gestational diabetes mellitus: a descriptive literature review.

This study attempted to review the evidence for or against the effectiveness of mobile health (m-health) interventions on health outcomes improvement and/or gestational diabetes mellitus (GDM) management. PubMed, Web of Science, Scopus, and Embase databases were searched from 2000 to 10 July 2018 to find studies investigating the effect of m-health on GDM management. After removing duplications, a total of 27 articles met our defined inclusion criteria. m-health interventions were implemented by smartphone, without referring to its type, in 26% (7/27) of selected studies, short message service (SMS) in 14.9% (4/27), mobile-based applications in 33.3% (9/27), telemedicine-based on smartphones in 18.5% (5/27), and SMS reminder system in 7.1% (2/27). Most of the included studies (n=23) supported the effectiveness of m-health interventions on GDM management and 14.3% (n=4) reported no association between m-health interventions and pregnancy outcomes. Based on our findings, m-health interventions could enhance GDM patients' pregnancy outcomes. A majority of the included studies suggested positive outcomes. M-health can be one of the most prominent technologies for the management of GDM.

Automated Misspelling Detection and Correction in Persian Clinical Text.

Accurate electronic health records are important for clinical care, research, and patient safety assurance. Correction of misspelled words is required to ensure the correct interpretation of medical records. In the Persian language, the lack of automated misspelling detection and correction system is evident in the medicine and health care. In this article, we describe the development of an automated misspelling detection and correction system for radiology and ultrasound's free texts in the Persian language. To achieve our goal, we used n-gram language model and three different types of free texts related to abdominal and pelvic ultrasound, head and neck ultrasound, and breast ultrasound reports. Our system achieved the detection performance of up to 90.29% for radiology and ultrasound's free texts with the correction accuracy of 88.56%. Results indicated that high-quality spelling correction is possible in clinical reports. The system also achieved significant savings during the documentation process and final approval of the reports in the imaging department.

ART Registries-Characteristics and experiences: A comparative study.

BACKGROUND: The incident of infertility is continuously increasing. As a result, the demand for medical care such as assisted reproductive technology (ART) technology is equally increasing. In order to manage the growing data and information collected on ART, there is a need for a registry system can provide accurate statistics about activities and outcomes and ensure the quality control. Therefore, the aim of this study was to examine and compare In vitro fertilization (IVF) and ART registries. METHODS: This is a descriptive-comparative study in which data from the national ART registries of 14 selected countries in 2018 were collected. In this study, databases such as PubMed, Web of Sciences, and Scopus, as well as Google Scholar websites were searched. RESULTS: Important aspects of the registry were studied. One of the most important goals of these systems is to collect information about ART, as well as to monitor and report the results and implications, and also implement new care plans. CONCLUSION: A national registry helps to better understand the scope and the effect of assisted reproduction on the health of infertile couples. By this registry system, different countries can compare the data with other countries, allowing the improvement of techniques and the best possible care for patients.