A Framework for Neonatal Prematurity Information System Development Based on a Systematic Review on Current Registries: An Original Research

Background

Registries are regarded as a just valuable fount of data on determining neonates suffering prematurity or low birth weight (LBW), ameliorating provided care, and developing studies.

Objective

This study aimed to probe the studies, including premature infants' registries, adapt the needed minimum data set, and provide an offered framework for premature infants' registries.

Material and Methods

For this descriptive study, electronic databases including PubMed, Scopus, Web of Science, ProQuest, and Embase/Medline were searched. In addition, a review of gray literature was undertaken to identify relevant studies in English on current registries and databases. Screening of titles, abstracts, and full texts was conducted independently based on PRISMA guidelines. The basic registry information, scope, registry type, data source, the purpose of the registry, and important variables were extracted and analyzed.

Results

Fifty-six papers were qualified and contained in the process that presented 51 systems and databases linked in prematurity at the popular and government levels in 34 countries from 1963 to 2017. As a central model of the information management system and knowledge management, a prematurity registry framework was offered based on data, information, and knowledge structure.

Conclusion

To the best of our knowledge, this is a comprehensive study that has systematically reviewed prematurity-related registries. Since there are international standards to develop new registries, the proposed framework in this article can be beneficial too. This framework is essential not only to facilitate the prematurity registry design but also to help the collection of high-value clinical data necessary for the acquisition of better clinical knowledge.

An evaluation of the quality of COVID-19 websites in terms of HON principles and using DISCERN tool

BACKGROUND

As many people relied on information from the Internet for official scientific or academically affiliated information during the COVID-19 pandemic, the quality of information on those websites should be good.

OBJECTIVE

The main purpose of this study was to evaluate a selection of COVID-19-related websites for the quality of health information provided.

METHOD

Using Google and Yahoo, 36 English language websites were selected, in accordance with the inclusion criteria. The two tools were selected for evaluation were the Health on the Net (HON) Code and the 16-item DISCERN tool.

RESULTS

Most websites (39%) were related to information for the public, and a small number of them (3%) concerned screening websites in which people could be informed of their possible condition by entering their symptoms. The result of the evaluation by the HON tool showed that most websites were reliable (53%), and 44% of them were very reliable. Based on the assessment results of the Likert-based 16-item DISCERN tool, the maximum and minimum values for the average scores of each website were calculated as 2.44 and 4.25, respectively.

CONCLUSION

Evaluation using two widely accepted tools shows that most websites related to COVID-19 are reliable and useful for physicians, researchers and the public.

Development and evaluation of a clinical decision support system for early diagnosis of acute appendicitis

The most frequent reason for individuals experiencing abdominal discomfort to be referred to emergency departments of hospitals is acute appendicitis, and the most frequent emergency surgery performed is an appendectomy. The purpose of this study was to design and develop an intelligent clinical decision support system for the timely and accurate diagnosis of acute appendicitis. The number of participants which is equal to 181 was chosen as the sample size for developing and evaluating neural networks. The information was gathered from the medical files of patients who underwent appendicectomies at Shahid Modarres Hospital as well as from the findings of their appendix samples' pathological tests. The diagnostic outcomes were then ascertained by the development and comparison of a Multilayer Perceptron network (MLP) and a Support Vector Machine (SVM) system in the MATLAB environment. The SVM algorithm functioned as the central processing unit in the Clinical Decision Support System (CDSS) that was built. The intelligent appendicitis diagnostic system was subsequently developed utilizing the Java programming language. Technical evaluation and system usability testing were both done as part of the software evaluation process. Comparing the output of the optimized artificial neural network of the SVM with the pathology result showed that the network's sensitivity, specificity, and accuracy were 91.7%, 96.2%, and 95%, respectively, in diagnosing acute appendicitis. Based on the existing standards and the opinions of general surgeons, and also comparing the results with the diagnostic accuracy of general surgeons, findings indicated the proper functioning of the network for the diagnosis of acute appendicitis. The use of this system in medical centers is useful for purposes such as timely diagnosis and prevention of negative appendectomy, reducing patient hospital stays and treatment costs, and improving the patient referral system.

Designing and Evaluating a Nutrition Recommender System for Improving Food Security in a Developing Country

BACKGROUND

Due to the increased price of foods in recent years and the diminished food security in Iran, nutrition recommender systems can suggest the most suitable and affordable foods and diets to users based on their health status and food preferences. Objective: The present study aimed to design and evaluate a recommender system to suggest healthy and affordable meals and provide a tele-nutrition consulting service.

METHODS

This applied three-phase study was conducted in 2020. In the first stage, the food items' daily prices were extracted from credible sources, and accordingly, meals were placed in three price categories. After conducting a systematic review of similar systems, the requirements and data elements were specified and confirmed by 10 nutritionists and 10 health information management and medical informatics experts. In the second phase, the software was designed and developed based on the findings. In the third phase, system usability was evaluated by four experts based on Nielsen's heuristic evaluation.

RESULTS

Initially, 72 meals complying with nutritional principles were placed in three price categories. Following a literature review and expert survey, 31 data elements were specified for the system, and the experts confirmed system requirements. Based on the information collected in the previous stage, the Web-based software TanSa in the Persian language was designed, developed, and presented on a unique domain. During the evaluation, the mean severity of the problems associated with Nielsen's 10 principles was 1.2, which is regarded as minor.

CONCLUSION

To promote food security, the designed system recommends healthy, nutritional, and affordable meals to individuals and households based on user characteristics.

Designing and evaluating the children's developmental motor disorders system: an experience from a developing country

BACKGROUND

Developmental disorders are a prevalent problem in the health sector of low- and middle-income countries (LMICs), and children in these countries are at greater risk. A registry system is helpful and vital to monitoring and managing this disease.

OBJECTIVE

The present study aims to develop an electronic registry system for children's developmental motor disorders.

METHODS

The study was conducted between 2019 and 2020 in three phases. First, the requirements of the system were identified. Second, UML diagrams were first drawn using Microsoft Visio software. Then, the system was designed using the ASP.NET framework in Visual Studio 2018, and the C# programming language was used in the NET 4.5 technology platform. In the third phase, system usability was evaluated from the users' viewpoint.

RESULTS

The findings of this research included system requirements, a conceptual model, and a web-based system. The client and system server connection was established through the IP/TCP communication protocol in a university physical network. End users approved the system with an agreement rate of 87.14%.

CONCLUSION

The study's results can be used as a model for designing and developing systems related to children's developmental movement disorders in other countries. It is also suggested as a valuable platform for research and improving the management of this disease.

Feasibility study and determination of prerequisites of telecare programme to enhance patient management in lung transplantation: a qualitative study from the perspective of Iranian healthcare providers

BACKGROUND

Non-adherence to treatment plans, follow-up visits and healthcare advice is a common obstacle in the management of lung transplant patients. This study aims to investigate experts' views on the needs and main aspects of telecare programmes for lung transplantation.

DESIGN

A qualitative study incorporating an inductive thematic analysis.

SETTING

Lung transplant clinic and thoracic research centre.

PARTICIPANTS

Clinicians: four pulmonologists, two cardiothoracic surgeons, two general physicians, two pharmacotherapists, one cardiologist, one nurse and one medical informatician.

METHOD

This study adopted a focus group discussion technique to gather experts' opinions on the prerequisites and features of a telecare programme in lung transplantation. All interviews were coded and combined into main categories and themes. Thematic analysis was performed to extract the key concepts using ATLAS.Ti. Ultimately, all extracted themes were integrated to devise a conceptual model.

RESULTS

Ten focus groups with 13 participants were conducted. Forty-six themes and subthemes were extracted through the thematic analysis. The main features of the final programme were extracted from expert opinions through thematic analysis, such as continuous monitoring of symptoms, drug management, providing a specific care plan for each patient, educating patients module, creating an electronic medical record to collect patient information, equipping the system with decision support tools, smart electronic prescription and the ability to send messages to the care team. The prerequisites of the system were summarised in self-care activities, clinician's tasks and required technologies. In addition, the barriers and benefits of using a telecare system to enhance the quality of care were determined.

CONCLUSION

Our investigation recognised the main factors that must be considered to design a telecare programme to provide ideal continuous care for lung transplant patients. Users should further explore the proposed model to support the development of telecare interventions at the point of care.

The Application of Knowledge-Based Clinical Decision Support Systems to Enhance Adherence to Evidence-Based Medicine in Chronic Disease

Among the technology-based solutions, clinical decision support systems (CDSSs) have the ability to keep up with clinicians with the latest evidence in a smart way. Hence, the main objective of our study was to investigate the applicability and characteristics of CDSSs regarding chronic disease. The Web of Science, Scopus, OVID, and PubMed databases were searched using keywords from January 2000 to February 2023. The review was completed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Then, an analysis was done to determine the characteristics and applicability of CDSSs. The quality of the appraisal was assessed using the Mixed Methods Appraisal Tool checklist (MMAT). A systematic database search yielded 206 citations. Eventually, 38 articles from sixteen countries met the inclusion criteria and were accepted for final analysis. The main approaches of all studies can be classified into adherence to evidence-based medicine (84.2%), early and accurate diagnosis (81.6%), identifying high-risk patients (50%), preventing medical errors (47.4%), providing up-to-date information to healthcare providers (36.8%), providing patient care remotely (21.1%), and standardizing care (71.1%). The most common features among the knowledge-based CDSSs included providing guidance and advice for physicians (92.11%), generating patient-specific recommendations (84.21%), integrating into electronic medical records (60.53%), and using alerts or reminders (60.53%). Among thirteen different methods to translate the knowledge of evidence into machine-interpretable knowledge, 34.21% of studies utilized the rule-based logic technique while 26.32% of studies used rule-based decision tree modeling. For CDSS development and translating knowledge, diverse methods and techniques were applied. Therefore, the development of a standard framework for the development of knowledge-based decision support systems should be considered by informaticians.

Evaluation of the PSO Metaheuristic Algorithm in Different Types of Sleep Apnea Diagnosis Using RR Intervals

Background

Sleep apnea is one of the most common sleep disorders that facilitating and accelerating its diagnosis will have positive results on its future trend.

Objective

This study aimed to diagnosis the sleep apnea types using the optimized neural network.

Material and Methods

This descriptive-analytical study was done on 50 cases of patients referred to the sleep clinic of Imam Khomeini Hospital in Tehran, including 11 normal, 13 mild, 17 moderate and 9 severe cases. At the first, the data were pre-processed in three stages, then The Electrocardiogram (ECG) signal was decomposed to 8 levels using wavelet transform convert and 6 nonlinear features for the coefficients of this level and 10 features were calculated for RR Intervals. For apnea categorizing classes, the multilayer perceptron neural network was used with the backpropagation algorithm. For optimizing Multi-layered Perceptron (MLP) weights, the Particle Swarm Optimization (PSO) evolutionary optimization algorithm was used.

Results

The simulation results show that the accuracy criterion in the MLP network is allied with the Backpropagation (BP) training algorithm for different types of apnea. By optimizing the weights in the MLP network structure, the accuracy criterion for modes normal, obstructive, central, mixed was obtained %96.86, %97.48, %96.23, and %96.44, respectively. These values indicate the strength of the evolutionary algorithm in improving the evaluation criteria and network accuracy.

Conclusion

Due to the growth of knowledge and the complexity of medical decisions in the diagnosis of the disease, the use of artificial neural network algorithms can be useful to support this decision.

Identification of Minimum Data Set of Neonatal Prematurity Information Management System for Iran

The Article Abstract is not available.  

Machine learning-based techniques to improve lung transplantation outcomes and complications: a systematic review

BACKGROUND

Machine learning has been used to develop predictive models to support clinicians in making better and more reliable decisions. The high volume of collected data in the lung transplant process makes it possible to extract hidden patterns by applying machine learning methods. Our study aims to investigate the application of machine learning methods in lung transplantation.

METHOD

A systematic search was conducted in five electronic databases from January 2000 to June 2022. Then, the title, abstracts, and full text of extracted articles were screened based on the PRISMA checklist. Then, eligible articles were selected according to inclusion criteria. The information regarding developed models was extracted from reviewed articles using a data extraction sheet.

RESULTS

Searches yielded 414 citations. Of them, 136 studies were excluded after the title and abstract screening. Finally, 16 articles were determined as eligible studies that met our inclusion criteria. The objectives of eligible articles are classified into eight main categories. The applied machine learning methods include the Support vector machine (SVM) (n = 5, 31.25%) technique, logistic regression (n = 4, 25%), Random Forests (RF) (n = 4, 25%), Bayesian network (BN) (n = 3, 18.75%), linear regression (LR) (n = 3, 18.75%), Decision Tree (DT) (n = 3, 18.75%), neural networks (n = 3, 18.75%), Markov Model (n = 1, 6.25%), KNN (n = 1, 6.25%), K-means (n = 1, 6.25%), Gradient Boosting trees (XGBoost) (n = 1, 6.25%), and Convolutional Neural Network (CNN) (n = 1, 6.25%). Most studies (n = 11) employed more than one machine learning technique or combination of different techniques to make their models. The data obtained from pulmonary function tests were the most used as input variables in predictive model development. Most studies (n = 10) used only post-transplant patient information to develop their models. Also, UNOS was recognized as the most desirable data source in the reviewed articles. In most cases, clinicians succeeded to predict acute diseases incidence after lung transplantation (n = 4) or estimate survival rate (n = 4) by developing machine learning models.

CONCLUSION

The outcomes of these developed prediction models could aid clinicians to make better and more reliable decisions by extracting new knowledge from the huge volume of lung transplantation data.

Validity and reliability of the Persian version of the Patient readiness to engage in health information technology (PRE-HIT) instrument

Background

The Patient readiness to engage in health information technology (PRE-HIT) is a conceptually and psychometrically validated questionnaire survey tool to measure willingness of patients with chronic conditions to use health information technology (HIT) resources.

Objectives

This study aimed to translate and validate a health information technology readiness instrument, the PRE-HIT instrument, into the Persian language.

Methods

A rigorous process was followed to translate the PRE-HIT instrument into the Persian language. The face and content validity was validated by impact score, content validity index (CVI) and content validity ratio (CVR). The instrument was used to measure readiness of 289 patients with chronic diseases to engage with digital health with a four point Likert scale. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) was used to check the validity of structure. The convergent and discriminant validity, and internal reliability was expressed by average variance extracted (AVE), construct reliability (CR), maximum shared squared variance (MSV), average shared square variance (ASV), and Cronbach's alpha coefficient. Independent samples, t-test and one-way ANOVA were used respectively to compare the impact of sex, education and computer literacy on the performance of all PRE-HIT factors.

Results

Eight factors were extracted: health information needs, computer anxiety, computer/internet experience and expertise, preferred mode of interaction, no news is good news, relationship with doctor, cell phone expertise, and internet privacy concerns. They explained 69% of the total variance and the KMO value was 0.79; Bartlett's test of sphericity was also statistically significant (sig < 0.001). The communality of items was higher than 0.5. An acceptable model fit of the instrument was achieved (CFI = 0.943, TLI = 0.931, IFI = 0.944, GFI = 0.893, RMSEA ≤ 0.06, χ2/df = 1.625, df = 292, P-value ≤ 0.001). The Cronbach's alpha coefficient achieved a satisfactory level of 0.729. The AVE for all factors was higher than 0.50 except for PMI (0.427) and CIEE (0.463) and also the CR for all factors was higher than 0.7, therefore, the convergent validity of the instrument is adequate. The MSV and ASV values for each factor were lower than AVE values; therefore, the divergent validity was acceptable.

Conclusion

The Persian version of the PRE-HIT was empirically proved for its validity to assess the level of readiness of patients to engage with digital health.

Design, implementation, and evaluation of an innovative intelligence information management system for premature infants

Introduction

Low birth weight is the most important condition of neonatal community health and the main cause of neonates' mortality. Identifying the indexes associated with this condition, and factors to prevent, and managing related data can help reduce the birth of premature infants to reduce the mortality rate due to this condition. The goal of present study was to design, implement and evaluate an innovative intelligence information management system for premature infants.

Material and method

The present study was a multidisciplinary research that was done in 2019 to 2021 in four integrated phases in Iran. The first phase aimed to compare the current status of registration systems of premature infants through a systematic review and semi-structured interviews by using the Delphi model Then the minimum data set was determined and was designed a proposed model based on it. In the second phase, the structure and how the user interacts with the system were determined, and, using Microsoft Visio software, Unified Modeling Language diagrams were drawn to define the logical relationship of data. In the third phase, the system was developed, and finally in the last phase, in three methods, users' views on the usability of the system were evaluated.

Results

The findings of this study included 233 essential data elements that were placed in two main groups of essential data, and the system was approved by end users for 87.73% consent and 67.19% satisfaction for SUMI (Software Usability Measurement Inventory) and 7.97 of 9 in QUIS questionnaire.

Conclusion

This research's results can be beneficial and functional such as a complete sample for design and development of other systems concerned to health systems.

Telemedicine in lung transplant to improve patient-centered care: A systematic review

OBJECTIVES

Long-term care combined with complex follow-up processes is among the essential needs of lung transplantation. Therefore, Telemedicine-based strategies can provide an effective approach for both patients and clinicians by applying remote patient monitoring. Hence, the main objective of this study was to investigate Telemedicine and telehealth usage in lung transplantation.

METHOD

A systematic review was conducted in four databases using keywords. Eligible studies were all English papers that developed Telemedicine-based programs to enhance patient care in lung organ transplantation. The interventions were analyzed analysis to determine the main descriptive areas. The quality of the included articles was evaluated using Mixed Methods Appraisal Tool (MMAT) tool by two authors.

RESULTS

Of the 261 retrieved articles, 27 met our inclusion criteria. Of these, 22 studies were devoted to the post-transplantation phase. All articles were published from 2002 to 2021 and the trend of publications has increased in recent years. Most of the studies were conducted in the United States and Canada. All eligible studies can be categorized into five types of Telemedicine interventions, 15 (55.56%) articles devoted to Telemonitoring, four (14.81%) for Teleconsultation, four (14.81%) articles for Telerehabilitation, three (11.11%) articles for Telespirometery, and one (3.70%) article were done regarding Tele-education.

CONCLUSION

This integrated review provides researchers with a new understanding of Telemedicine-based care solutions. Findings show that remote patient care in lung transplantation includes various aspects, especially self-care improvement.

The silent crisis of child abuse in the COVID‐19 pandemic: A scoping review

Background and Aims

The global outbreak of COVID‐19 has become an international concern. The lives of children are severely affected by COVID‐19 pandemic. There is evidence of a pandemic impact on violence against children. This scoping review study aimed to investigate the effects of the COVID‐19 pandemic on child abuse.

Methods

We searched PubMed, Scopus, and Web of Science databases to retrieve related studies. Regarding the recent incident of COVID‐19, the articles were reviewed from 2019 to June 1, 2021. The terms Child abuse and COVID‐19 were used in the precise search technique of each database. The search techniques were created to work with any scientific database that used the keywords given.

Results

In the initial search of scientific databases, 568 articles were retrieved. After applying the inclusion and exclusion criteria during the screening process, 16 papers were included in the scoping review. Twelve articles have mentioned the increase of physical, psychological, and neglect types of abuse. However, sexual violence has not been reported in any of the articles. Four articles reported a reduction in the incidence of child abuse.

Conclusion

During the COVID‐19 pandemic, a crisis occurred in the form of an upsurge in violence toward children, since limits made to diminish the virus, in general, increased the danger to children. Numerous factors such as stress, poverty, financial situation, history of violence, school closures, and lack of contact with support organizations contribute to this phenomenon. Social action and support needed is the right of every child in need in this critical situation.

Pragmatic solutions to enhance self-management skills in solid organ transplant patients: systematic review and thematic analysis

Background

In organ transplantation, all patients must follow a complex treatment regimen for the rest of their lives. Hence, patients play an active role in the continuity of the care process in the form of self-management tasks. Thus, the main objective of our study was to investigate the pragmatic solutions applied by different studies to enhance adherence to self-management behaviors.

Method

A systematic review was conducted in five databases from 2010 to August 2021 using keywords. Eligible studies were all English papers that developed self-management programs to enhance patient care in solid organ transplantation. The interventions were analyzed using thematic analysis to determine the main descriptive areas. The quality of the included articles was evaluated using the research critical appraisal program (CASP) tool.

Results

Of the 691 retrieved articles, 40 met our inclusion criteria. Of these, 32 studies were devoted to the post-transplantation phase. Five main areas were determined (e-health programs for telemonitoring, non-electronic educational programs, non-electronic home-based symptom-monitoring programs, electronic educational plans for self-monitoring, and Telerehabilitation) according to thematic analysis. Most studies (72.5%) declared that developed programs and applied solutions had a statistically significant positive impact on self-management behavior enhancement in transplant patients.

Conclusion

The results showed that an effective solution for improving organ transplantation needs patient collaboration to address psychological, social, and clinical aspects of patient care. Such programs can be applied during candidate selection, waiting list, and after transplantation by putting the patient at the center of care.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12875-022-01766-z.

Global perspective on pediatric growth hormone registries: a systematic review

BACKGROUND

Registries are considered valuable data sources for identification of pediatric conditions treated with growth hormone (GH), and their follow-up. Currently, there is no systematic literature review on the scope and characteristics of pediatric GH registries. Therefore, the purpose of this systematic review is to identify worldwide registries reported on pediatric GH treatment and to provide a summary of their main characteristics.

CONTENT

Pediatric GH registries were identified through a systematic literature review. The search was performed on all related literature published up to January 30th, 2021. Basic information on pediatric GH registries, their type and scope, purpose, sources of data, target conditions, reported outcomes, and important variables were analyzed and presented.

SUMMARY

Twenty two articles, reporting on 20 pediatric GH registries, were included in this review. Industrial funding was the most common funding source. The main target conditions included in the pediatric GH registries were: growth hormone deficiency, Turner syndrome, Prader Willi syndrome, small for gestational age, idiopathic short stature, and chronic renal insufficiency. The main objectives in establishing and running pediatric GH registries were assessing the safety and effectiveness of the treatment, describing the epidemiological aspects of target growth conditions and populations, serving public health surveillance, predicting and measuring treatment outcomes, exploring new and useful aspects of GH treatment, and improving the quality of patient care.

OUTLOOK

This systematic review provides a global perspective on pediatric GH registries which can be used as a basis for the design and development of new GH registry systems at both national and international levels.

Development and validation of the Iranian Neonatal Prematurity Minimum Data Set (IMSPIMDS): a systematic review, focus group discussion, and Delphi technique

Background: Information systems help to collect information about patients. The minimum data set (MDS) provides the basis for decision-making. Objectives: This study was conducted to determine the comprehensive national MDS for prematurity information management system (IMSPIMDS) in Iran. Methods: This research is a cross-sectional study with three steps including systematic review, focus group discussion, and Delphi technique. A systematic review was conducted in relevant databases. Then a focus group discussion was used to classify the extracted data elements by contributing specializing in various fields experts. Finally, MDSs were chosen through the decision Delphi technique in two rounds. Collected data were analyzed using IBM statistics SPSS 26. Results: In total, 233 data elements were included in the Delphi survey. The data elements based on the experts’ opinions, were classified into two main categories including maternal and newborn. The final data elements categories were 107 and 126. Conclusions: The existence of national MDS as the core of the premature newborn surveillance program is essential and leads to appropriate decisions. We developed and internally validated a minimum data set for prematurity researches. This study generated new knowledge to enable healthcare systems professionals to collect relevant and meaningful. The use of this standardized approach can help benchmark clinical practice and target improvements worldwide.10.32598/jpr.10.1.986.1

Myocardial Infarction Prediction and Estimating the Importance of its Risk Factors Using Prediction Models

Background

According to World Health Organization (WHO), cardiovascular diseases (CVDs) are the leading cause of death globally. Although significant progress has been made in the diagnosis of CVDs, more investigation can be helpful. Therefore, this study aimed to predict the risk of myocardial infarction (MI) using data mining algorithms.

Methods

The applied data were related to the admitted patients in Rajaei specialized cardiovascular hospital located in Tehran. At first, a literature review and interview with a cardiologist were conducted to understand MI. Then, data preparation (cleaning and normalizing the data) was performed. After all, different classification algorithms were applied in IBM SPSS Modeler (14.2) software on the prepared data; and, power of the applied algorithms and the importance of the risk factors in predicting the probability of getting involved with MI was calculated in the mentioned software.

Results

This study was able to predict MI % 75.28 and 77.77% in terms of accuracy and sensitivity, respectively. The results also revealed that cigarette consumption, addiction, blood pressure, and cholesterol were the most important risk factors in predicting the probability of getting involved with MI, respectively.

Conclusions

Predicting studies aim to support rather than replace clinical judgment. Our prediction models are not sufficiently accurate to supplant decision-making by physicians but have considerable tips about MI risk factors.

Designing an Intelligent System for Diagnosing Type of Sleep Apnea and Determining Its Severity

Introduction: Sleep apnea syndrome can be considered as one of the most serious risk factors of sleep disorder. Due to the lack of information about this disease, many causes of unexpected deaths have been identified. With increasing the number of patients with this disease around the world, many patients suffer apnea complications.  Most of them are not treated because of the complex and costly and time-consuming polysomnography (PSG) diagnostic procedure.Material and Methods: This descriptive-analytical study was performed on 50 patients referred to sleep clinic of Imam Khomeini Hospital in Tehran,  Attempts to design, and develop a system for detection of sleep apnea and its severity using ECG signals, RR intervals and airflow. The random forest algorithm and MATLAB2016 were used in the design of the system that the algorithm inputs are extracted 8 features nonlinear in time-frequency domain from airflow and ECG signals and 10 nonlinear features of RR intervals.Results: The accuracy for normal, obstructive, central and mixed apnea was obtained at 95.3%, 97.92%, 99.60%, and 97.29%, respectively, and the accuracy For detection of normal, mild, moderate and severe apnea was obtained 96%, 94%, 94%, 96% respectively. According to the results, the proposed system can correctly classify the types of sleep apnea and its severity.Conclusion: The proposed system, which has high performance capability in addition to increasing the physician speed and accuracy in the diagnosis of apnea can be used in home systems and the areas where healthcare facilities are not sufficient.

Clinical decision support systems-based interventions to improve medication outcomes: A systematic literature review on features and effects

Background: Clinical decision support systems (CDSSs) interventions were used to improve the life quality and safety in patients and also to improve practitioner performance, especially in the field of medication. Therefore, the aim of the paper was to summarize the available evidence on the impact, outcomes and significant factors on the implementation of CDSS in the field of medicine. Methods: This study is a systematic literature review. PubMed, Cochrane Library, Web of Science, Scopus, EMBASE, and ProQuest were investigated by 15 February 2017. The inclusion requirements were met by 98 papers, from which 13 had described important factors in the implementation of CDSS, and 86 were medicated-related. We categorized the system in terms of its correlation with medication in which a system was implemented, and our intended results were examined. In this study, the process outcomes (such as; prescription, drug-drug interaction, drug adherence, etc.), patient outcomes, and significant factors affecting the implementation of CDSS were reviewed. Results: We found evidence that the use of medication-related CDSS improves clinical outcomes. Also, significant results were obtained regarding the reduction of prescription errors, and the improvement in quality and safety of medication prescribed. Conclusion: The results of this study show that, although computer systems such as CDSS may cause errors, in most cases, it has helped to improve prescribing, reduce side effects and drug interactions, and improve patient safety. Although these systems have improved the performance of practitioners and processes, there has not been much research on the impact of these systems on patient outcomes.

Suggesting a framework for preparedness against the pandemic outbreak based on medical informatics solutions: a thematic analysis

Background

When an outbreak emerged, each country needs a coherent and preventive plan to deal with epidemics. In the era of technology, adopting informatics‐based solutions is essential. The main objective of this study is to propose a conceptual framework to provide a rapid and responsive surveillance system against pandemics.

Methods

A three‐step approach was employed in this research to develop a conceptual framework. These three steps comprise (1) literature review, (2) extracting and coding concepts, and determining main themes based on thematic analysis using ATLAS.ti® software, and (3) mapping concepts. Later, all of the results synthesized under expert consultation to design a conceptual framework based on the main themes and identified strategies related to medical informatics.

Results

In the literature review phase, 65 articles were identified as eligible studies for analysis. Through line by line coding in thematic analysis, more than 46 themes were extracted as potential foremost themes. Based on the key themes and strategies were employed by studies, the proposed framework designed in three main components. The most appropriate strategies that can be used in each section were identified based on the demands of each part and the available solutions. These solutions were employed in the final framework.

Conclusion

The presented model in this study can be the first step for a better understanding of the potential of medical informatics solutions in promoting epidemic disease management. It can be applied as a reference model for designing intelligent surveillance systems to prepare for probable future pandemics.

Developing an apnea-hypopnea diagnostic model using SVM

Introduction: Among sleep-related disorders, Sleep apnea has been under more attention and it’s the most common respiratory disorder in which respiration ceases frequently which can lead to serious health disorders and even mortality. Polysomnography is the standard method for diagnosing this disease at the moment which is costly and time-consuming. The aim of the present study was to analyze essential signals for the diagnosis of sleep apnea.Method: This analytical–descriptive was conducted on 50 patients (11 normal, 13 mild, 17 moderate and 9 severe patients) in the sleep clinic of Imam Khomeini hospital. Initially, data pre-processing was carried out in two steps(and Moving Average algorihtm). Next, using the SVD method, 12 features were extracted for airflow. Finally, to classify data, SVM with Quadratic, Polynomia and RBF kernels were trained and tested.Results: After applying different kernel functions on SVM, the RBF kernel showed the most efficient performance. After running the RBF kernel function ten times, the mean accuracy obtained for normal, apnea, and hypopnea modes were 92.74%, 91.70%, 93.26%.Conclusion: The results indicate that in online applications or applications in which volume and time calculations and the result are important simultaneously, patients could be diagnosed with acceptable accuracy using machine learning algorithms. 

Potential biomarker detection for liver cancer stem cell by machine learning approach

Objectives: In this study, we aimed to identify putative biomarkers for identification and characterization of these cells in liver cancer. Methods: We employed a supervised machine learning method, XGBoost, to data from 13 GEO data series to classify samples using gene expression data. Results.  Across the 376 samples (129 CSCs and 247 non-CSCs cases), XGBoost displayed high performance in the classification of data. XGBoost feature importance scores and SHAP (Shapley Additive explanation) values were used for the interpretation of results and analysis of individual gene importance. We confirmed that expression levels of a 10-gene set (PTGER3, AURKB, C15orf40, IDI2, OR8D1, NACA2, SERPINB6, L1CAM, SMC1A, and RASGRF1) were predictive. The results showed that these 10 genes can detect CSCs robustly with accuracy, sensitivity, and specificity of 97 %, 100 %, and 95 %, respectively. Conclusions. We suggest that the ten-gene set may be used as a biomarker set for detecting and characterizing CSCs using gene expression data.

Mobile Health Applications for Osteoporosis Support Available on the Market: A Systematic Review

Introduction: The use of mobile applications (apps) become widespread and Provide many benefits especially in healthcare. According to the World Health Organization, osteoporosis is one of the most common diseases of elderly in the world. Like other chronic conditions, disease self-management can prove fruitful. Using a mobile application for Osteoporosis can improve patient care and self-management by encouraging patients to take a more active role in their health.Material and Methods: This study presents a systematic review of mHealth applications, available on Google Play Store, Bazaar market (as a local market) and also Apple App Store, for both the English and Persian speakers. The assessment criteria, including content, visual aids, reminders, health warnings, social and design of selected apps, were tested during July 2019.Results: Reviewing the 19 included applications showed that the most and least focus of apps content was on exercise with 84% repetition and the osteoporosis fracture that no program addressed this issue separately. Findings on reminders, health warnings, and visual aids were not very encouraging (available in 11% apps). Reminders were more common in English-speaking apps than Persian-speaking ones, and Visual aids, one of the benefits of mobile apps over paper logbooks, were provided only in2 apps. The opportunity to share data in social networks was available in 26% of apps, and in the design section, most of the apps have no significant flaws, but 74% of cases did not provide any clear instructions required for the elderly.Conclusion:The review shows that there are rather few products on offer and the ones that are available display low quality, poor performance, and evidence-based information is also insufficiently used. Further efforts are required to collect data that will support the design of validated evidence-based educational functions for mHealth apps.

Development and validation of the Neonatal Abstinence Syndrome Minimum Data Set (NAS-MDS): a systematic review, focus group discussion, and Delphi technique

OBJECTIVES

Neonatal abstinence syndrome (NAS) is a combination of symptoms in infants exposed to any variety of substances in utero. Information systems and registries help to collect information about these patients; however, there is always a deep gap between complete and accurate information to be collected, understood, and applied in the health care system; thus, defining a minimum data sets (MDS) as one of the primarily steps of designing a registry system is essential. The aim of this study was to develop an MDS of the registry for infants with NAS in Iran.

METHODS

This research is a descriptive cross-sectional study. In this study, three steps were carried out to develop the MDS including systematic review, Delphi technique, and focus group discussion. A systematic review was conducted in relevant databases to identify appropriate related data. In the second phase, a focus group discussion was used to classify the extracted data elements by contributing neonatologists. Finally, data elements were chosen through the decision Delphi technique in two distinct rounds. Collected data were analyzed using SPSS 22 (SPSS Inc., Chicago, IL).

RESULTS

By reviewing related papers and available NAS registries in other countries, 145 essential data elements were identified. They were classified into two main categories based on the eight experts' opinions including maternal with two sections and infant with two sections. After applying two rounds of Delphi technique, the final data elements for maternal and infant categories were 42 and 31, respectively. Thus, on completion of the survey, 73 data elements were approved.

CONCLUSION

The proposed MDS for NAS can help to store an accurate and comprehensive data, document medical records, integrate them with other information systems and registries, and communicate with other healthcare providers and healthcare centers. This MDS can contribute to the provision of high-quality care and better clinical decisions.

Management of the essential data element in the differential diagnosis of oral medicine: An effective step in promoting oral health

BACKGROUND

Oral soft tissue diseases include a broad spectrum, and the wide array of patient data elements need to be processed in their diagnosis. One of the biggest and most basic challenges is the analysis of this huge amount of complex patient data in an increasing number of complicated clinical decisions. This study seeks to identify the necessary steps for collecting and management of these data elements through establishing a consensus-based framework.

METHODS

This research was conducted as a descriptive, cross-sectional study from April 2016 to January 2017, which has been performed in several steps: literature review, developing the initial draft (v. 0), submitting the draft to experts, validating by an expert panel, applying expert opinions and creating version v.i, performing Delphi rounds, and creating the final framework.

RESULTS

The administrative data category with 17 and the historical data category with 23 data elements were utilized in recording data elements in the diagnosis of all of the different oral diseases. In the paraclinical indicator and clinical indicator categories, the necessary data elements were considered with respect to the 6 main axes of oral soft tissue diseases, according to Burket's Oral Medicine: ulcerative, vesicular, and bullous lesions; red and white lesions of the oral mucosa; pigmented lesions of the oral mucosa; benign lesions of the oral cavity and the jaws; oral and oropharyngeal cancer; and salivary gland diseases.

CONCLUSIONS

The study achieved a consensus-based framework for the essential data element in the differential diagnosis of oral medicine using a comprehensive search with rich keywords in databases and reference texts, providing an environment for discussion and exchange of ideas among experts and the careful use of the Delphi decision technique.

A Multi-Organ Nucleus Segmentation Challenge

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.

Identification and Validation of Requirements for a Registry System of Children's Developmental Motor Disorders in Iran

BACKGROUND

 Despite recent advances in the field of medical sciences, children's developmental motor disorders (DMDs) are considered as one of the challenges in this area. Establishment of electronic systems for recording and monitoring children's DMDs can play an effective role in identifying patients and reducing the costs and consequences of the disease management. The aim of this study was to identify and validate the requirements for a registry system of children's DMDs in Iran.

METHODS

 The present descriptive-analytical study was performed in three main stages. In the first step, the literature was reviewed to identify the requirements. In the second stage, the information obtained from the literature review was used to develop a questionnaire for validating and selecting the requirements for an electronic system of recording DMDs in infants. In the final stage, the requirements were validated by selected experts (22 specialists). Data were analyzed using SPSS 20 software (IBM Corporation, New York, United States).

RESULTS

 According to findings, the requirements of a registry system for children's DMDs were identified in three areas of demographic (24 data elements), clinical data (87 data elements), and technical (28 capabilities). In the demographic section, data elements of "family history of motor disorders" (mean = 1.18) and "drug allergy" (mean = 2.9) gained an average score of < 2.5 and therefore were not selected as data elements necessary for the registry system of data recording and monitoring children's DMDs.

CONCLUSION

 In such developing countries as Iran, standard information recording and management is not properly done due to a large amount of information and the lack of comprehensive information registry systems. The findings of this study can help to design and establish information registry systems in the field of children's DMDs. Based on the findings of this research, it is recommended that future research be done to explore infrastructures necessary for providing a suitable platform to design and implement information registry systems in the field of children's DMDs.

Mobile health applications for improving the sexual health outcomes among adults with chronic diseases: A systematic review

Aims

Chronic diseases may affect sexual health as an important factor for well-being. Mobile health (m-health) interventions have the potential to improve sexual health in patients with chronic conditions. The aim of this systematic review was to summarise the published evidence on mobile interventions for sexual health in adults with chronic diseases.

Methods

Five electronic databases were searched for English language peer-reviewed literature from 1 January 2009 to 31 December 2019. Appropriate keywords were identified based on the study's aim. Study selection was based on the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. The full texts of potential studies were reviewed, and final studies were selected. The m-health evidence reporting and assessment (mERA) checklist was used to assess the quality of the selected studies. After data extraction from the studies, data analysis was conducted.

Results

Nine studies met the inclusion criteria. All interventions were delivered through websites, and a positive effect on sexual problems was reported. Prostate and breast cancer were considered in most studies. Interventions were delivered for therapy, self-help and consultation purposes. Quality assessment of studies revealed an acceptable quality of reporting and methodological criteria in the selected studies. Replicability, security, cost assessment and conceptual adaptability were the criteria that had not been considered in any of the reviewed studies.

Conclusions

Reviewed studies showed a positive effect of mobile interventions on sexual health outcomes in chronic patients. For more effective interventions, researchers should design web-based interventions based on users' needs and consider the m-health essential criteria provided by mERA. Additionally, mobile interventions can be more effective in combination with smartphone apps.

Exploring and Prioritization of Mobile-Based Self-Management Strategies for HIV Care

OBJECTIVE

Appropriate mobile-based self-management strategies can be as new approaches to decelerate the HIV infection progression and improve the quality of life. This study aims at (i) identifying in the literature mobile-based self-management strategies for HIV care and (ii) prioritizing those from the point of view of infectious diseases specialists. This study provides some clues to design useful mobile-based self-management tools for HIV patients, from the point of view of practitioners.

METHODS

This mixed methods study was done in two main phases. In the first phase, a review was conducted in: PubMed, Web of Science, Science Direct, Scopus, and Ovid. In this manner, related studies published between 2010 and 2017 and in the English language were reviewed. In the second phase, identified mobile-based self-management strategies were scored and prioritized by 23 participants. Frequency distribution and mean reports were calculated using SPSS statistical software.

RESULTS

By detailed reviewing of 24 related articles, the HIV mobile-based self-management strategies were identified in 47 categories and subcategories. According to the findings, "enhance the quality of life" was the main self-management strategy addressed by reviewed studies. However, "antiretroviral therapy and medication adherence" was reported at a higher rate to be a more helpful strategy than "enhance the quality of life".

CONCLUSION

In this study, helpful HIV mobile-based self-management strategies were identified that can be used to guide self-management interventions which have the potential to improve the healthcare services for people living with HIV.

A Systematic Review of Nutrition Recommendation Systems: With Focus on Technical Aspects

Background:

Nutrition informatics has become a novel approach for registered dietitians to practice in this field and make a profit for health care. Recommendation systems considered as an effective technology into aid users to adjust their eating behavior and achieve the goal of healthier food and diet. The purpose of this study is to review nutrition recommendation systems (NRS) and their characteristics for the first time.

Material and Methods:

The systematic review was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The process of articles selection was based on the PRISMA strategy. We identified keywords from our initial research, MeSH database and expert’s opinion. Databases of PubMed, Web of Sciences, Scopus, Embase, and IEEE were searched. After evaluating, they obtained records from databases by two independent reviewers and inclusion and exclusion criteria were applied to each retrieved work to select those of interest. Finally, 25 studies were included.

Results:

Hybrid recommender systems and knowledge-based recommender systems with 40% and 32%, respectively, were the mostly recommender types used in NRS. In NRS, rule-based and ontology techniques were used frequently. The frequented platform that applied in NRS was a mobile application with 28%.

Conclusion:

If NRS was properly designed, implemented and finally evaluated, it could be used as an effective tool to improve nutrition and promote a healthy lifestyle. This study can help to inform specialists in the nutrition informatics domain, which was necessary to design and develop NRS.

Better monitoring of abused children by designing a child abuse surveillance system: Determining national child abuse minimum data set

BACKGROUND

Monitoring the trend of child abuse can significantly help in measuring the magnitude of the problem and understanding its recurrence. The minimum data set (MDS) is a set of elements of each domain that provides the basis for decision-making. This study was conducted to determine the comprehensive national minimum data set for child abuse surveillance system (CASS) in Iran.

METHODS

This is a cross-sectional descriptive study. Data were gathered from the selected countries and child abuse registry and surveillance systems. The MDS questionnaire was designed based on a review of the publications and experts' opinions. The final data elements of the CASS were determined using the Delphi technique by visiting pediatricians.

RESULTS

In total, 147 data elements were included in the Delphi survey. The data elements of the CASS were classified into seven categories as follows: demographic data, incident related data, medical history, diagnostic tests, incident nature, therapeutic measures, and other required data.

CONCLUSION

The existence of national MDS as the core of the child abuse surveillance program is essential and leads to appropriate decisions in this regard. The MDS can meet the needs of professionals, decision makers, researchers, and policymakers who decide on reducing the incidence of child abuse.

Identification and ranking of important bio-elements in drug-drug interaction by Market Basket Analysis

Introduction: Drug-drug interactions (DDIs) are the main causes of the adverse drug reactions and the nature of the functional and molecular complexity of drugs behavior in the human body make DDIs hard to prevent and threat. With the aid of new technologies derived from mathematical and computational science, the DDI problems can be addressed with a minimum cost and effort. The Market Basket Analysis (MBA) is known as a powerful method for the identification of co-occurrence of matters for the discovery of patterns and the frequency of the elements involved. Methods: In this research, we used the MBA method to identify important bio-elements in the occurrence of DDIs. For this, we collected all known DDIs from DrugBank. Then, the obtained data were analyzed by MBA method. All drug-enzyme, drug-carrier, drug-transporter and drug-target associations were investigated. The extracted rules were evaluated in terms of the confidence and support to determine the importance of the extracted bio-elements. Results: The analyses of over 45000 known DDIs revealed over 300 important rules from 22 085 drug interactions that can be used in the identification of DDIs. Further, the cytochrome P450 (CYP) enzyme family was the most frequent shared bio-element. The extracted rules from MBA were applied over 2000000 unknown drug pairs (obtained from FDA approved drugs list), which resulted in the identification of over 200000 potential DDIs. Conclusion: The discovery of the underlying mechanisms behind the DDI phenomena can help predict and prevent the inadvertent occurrence of DDIs. Ranking of the extracted rules based on their association can be a supportive tool to predict the outcome of unknown DDIs.

Key security and privacy issues from implementing the National Electronic Health Record in the Islamic Republic of Iran

Background

In countries that have not implemented Electronic Health Records (EHR) comprehensively, international organizations are important steps in the development of EHR.

Aims

The objective of this study was to compare different dimensions of privacy in the EHR systems in terms of the following standards organizations: ASTM, Health Level Seven (HL7), and International Organization for Standardization (ISO), in order to create a security and privacy model for EHR.

Methods

This study was done in two steps: 1) survey of standards organizations, and 2) compare standards in comparative tables.

Results

Standards 12, 1 and 5 were extracted from the ASTM, HL7 and ISO respectively.

Conclusions

Evidence shows that the goal of standards was to create EHR systems that identified not only the access level of users, but taking consent for reveal information of people and also approved data by authorized persons in a secure framework. In this regard, ASTM looks comprehensive for privacy issues, while ISO18308 focuses on security issues and data interoperability simultaneously, while Hl7 has emphasized access.

The necessity to develop a national classification system for Iranian traditional medicine

BACKGROUND

Classification of disease and interventions in traditional medicine (TM) is necessary for standardised coding of information. Currently, in Iran, there is no standard electronic classification system for disease and interventions in TM.

OBJECTIVE

The current study aimed to develop a national framework for the classification of disease and intervention in Persian medicine based on expert opinion.

METHOD

A descriptive cross-sectional study was carried out in 2018. The existing systems for the classification of disease and interventions in TM were reviewed in detail, and some of the structural and content characteristics were extracted for the development of the classification of Iranian traditional medicine. Based on these features, a self-administered questionnaire was developed. Study participants (25) were experts in the field of Persian medicine and health information management in Tehran medical universities.

RESULTS

Main axes for the classification of disease and interventions were determined. The most important applications of the classification system were related to clinical coding, policymaking, reporting of mortality and morbidity data, cost analysis and determining the quality indicators. Half of the participants (50%) stated that the classification system should be designed by maintaining the main axis of the World Health Organization classification system and changing the subgroups if necessary. A computer-assisted coding system for TM was proposed for the current study.

CONCLUSION

Development of this classification system will provide nationally comparable data that can be widely used by governments, national organisations and academic researchers.

Current approaches in identification and isolation of cancer stem cells

Cancer stem cells (CSCs) are tumor cells with initiating ability, self-renewal potential, and intrinsic resistance to conventional therapeutics. Efficient isolation and characterization of CSCs pave the way for more comprehensive knowledge about tumorigenesis, heterogeneity, and chemoresistance. Also a better understanding of CSCs will lead to novel era of both basic and clinical cancer research, reclassification of human tumors, and development of innovative therapeutic strategies. Finding novel diagnostic and effective therapeutic strategies also enhance the success of treatment in cancer patients. There are various methods based on the characteristics of the CSCs to detect and isolate these cells, some of which have recently developed. This review summarized current techniques for effective isolation and characterization of CSCs with a focus on advantages and limitations of each method with clinical applications.

Design of Infertility Monitoring System: Minimum Data Set Approach

Reproductive health is vital for human and infertility is also one of the most important challenges in the reproductive system. Infertility is one of the most common chronic health disorders, regardless of age. The Minimum Data Set (MDS) helps to manage infertility by monitoring and evaluating infertility interventions based on collecting data.

The development of MDS is an essential objective in order to implement an infertility monitoring system for the creation of standardized and effective data management through the provision of comprehensive and identical data elements for infertility.

This is a descriptive cross-sectional study conducted in 2017. The data has been collected from infertility clinics in the world, as well as WHO, CDC, ASRM, and ESHRE reports.

In order to decide on data elements, the Delphi technique was used using a questionnaire that contained data elements which were distributed among 12 experts including one reproductive endocrinology and infertility fellow, six obstetrician-gynecologists, two reproductive biologists, two urologists and one community medicine specialist using the 5 point Likert scale. The questionnaire was divided into two categories: managerial and clinical, each with 4 sections, and 60 and 940 data elements, respectively.

MDS is an essential tool for evaluating the infertility process. Using this tool will provide an opportunity to develop a set of quality care criteria that can be used to ensure the quality of infertility care.

Neonatal abstinence syndrome: a systematic review of current databases and registries

OBJECTIVES

Registries are considered as rich sources of data for determination of infants with neonatal abstinence syndrome (NAS), the improvement of provided care and research. The aims of this study were: (1) to investigate the existing studies including NAS registries, (2) to identify and extract the required data elements.

METHODS

The following electronic databases were searched: PubMed, Scopus, Web of Science, ProQuest, Embase/Medline, and Psych Info. In addition, a review of gray literature was undertaken to identify relevant studies in English covering the period from 1 January 2009 to 1 November 2018 including registries and databases. Screening of titles, abstracts, and full-texts were conducted independently by two researchers based on PRISMA guidelines. The basic registry information, scope, registry type, data source, the purpose of registry, important variables were extracted and analyzed.

RESULTS

Twenty-five articles were eligible and included in the review; they reported 37 registries and databases related to NAS at the national and state levels in 11 countries from 1876 to 2013. We proposed a NAS registry design framework based on well-known data-information-knowledge (DIK) structure due to Ackoff's DIK hierarchy has a defined role as a central model of information systems, information management, and knowledge management.

CONCLUSIONS

To the best of our knowledge, this is the first study which has systematically reviewed NAS-related registries. Since there are no international standards to develop new NAS registries, the proposed framework in this article can be beneficial. This framework is essential not only to facilitate the NAS registry design but also to help the collection of high-value clinical data necessary for the acquisition of better clinical knowledge.

Development A Guideline-based Decision Support System to Diagnosis of Primary Immunodeficiency Diseases

Introduction: Primary immunodeficiency diseases (PID) are generally rare genetic disorders affecting the immune system. Overlapping PIDs symptoms and signs is a challenge to diagnosis and treatment. On the other hand, remembering of all diagnosis criteria is difficult for practitioners. The purpose of this research is developing guideline-based clinical decision support system for diagnosis of primary immune deficiency diseases, to assist practitioner in order to diagnose of disease in early stage and to minimize complications of such diseases.Material and Methods: To provide data a checklist was used and most important demographic information, symptoms, family history, physical findings and laboratory findings to diagnose eight common PIDs extracted from guidelines and literature under specialists opinion. The diagnosis inference model design and develop in Protégé (version 3.4.8) frame based ontology modeling using "Noy and McGuinness" method. Then the mobile based inference model in Eclipse (SDK version 3.7.1) software has been developed and clinical decision support system of primary immunodeficiency has been created.Results:  To design the diagnosis inference model in Protégé software, data were classified in 5 main classes and 24 subclasses as hierarchical. Then, specific properties of each class, and determine the value of each property. Then define Instances of each class and initialized instance properties. Then use this model to develop CDSS based on mobile in Eclipse software. At the end, the inference model and the CDSS test with 110 patient’s record data and both of them recognized all 110 patient correctly such as specialist recognition.Conclusion:Guideline-based decision support systems help to detect diseases correctly, quickly and early. Guideline-based decision support systems are very reliable to practitioner, because guidelines are accepted to their. These systems reduce the forget probability of diagnosis stages and percentage error of diagnosis by practitioner and increase the accuracy of diagnosis.

Developing a new intelligent system for the diagnosis of oral medicine with case-based reasoning approach

OBJECTIVE

Since the clinical manifestations of many oral diseases can be quite similar despite the wide variety in etiology and pathology, the differential diagnosis of oral diseases is a complex and challenging process. Intelligent system for differential diagnosis of oral medicine using the artificial intelligence (AI) capabilities helps specialists in achieving differential diagnosis in a wide range of oral diseases.

MATERIALS AND METHODS

First, the essential data elements to design and develop an intelligent system were identified in a cross-sectional descriptive study. The case-based reasoning method was selected to design and implement the system, which consists of three stages: collect the clinical data, construct the cases database, and case-based reasoning cycle. The problem is solved by CBR method in a cycle consisting of four main stages of retrieval, reuse, review, and retention. The evaluation process was conducted in a pilot-based way through the evaluation of the system's performance in the clinical setting and also using the usability assessment questionnaire.

RESULTS

The output of the present project is a web-based intelligent information system, which is developed using the Visual Studio 2015 software. The database of this system is the Microsoft SQL Server version 2012, which has been programmed based on Net framework (version 4.5 or higher) using Visual Basic language. The results of the system evaluation by specialists in clinical settings showed that the system's diagnosis power in different aspects of the disease is influenced by their prevalence and incidence.

CONCLUSIONS

System development using the artificial intelligence capabilities and through the clinical data analysis has potential to help specialists to determine the best diagnostic strategy to achieve a differential diagnosis of a wide range of oral diseases. The results of evaluation present the potential of the system to improve the quality and efficiency of patient care.

Words prediction based on N-gram model for free-text entry in electronic health records

The process of documentation is one of the most important parts of electronic health records (EHR). It is time-consuming, and up until now, available documentation procedures have not been able to overcome this type of EHR limitations. Thus, entering information into EHR still has remained a challenge. In this study, by applying the trigram language model, we presented a method to predict the next words while typing free texts. It is hypothesized that using this system may save typing time of free text. The words prediction model introduced in this research was trained and tested on the free texts regarding to colonoscopy, transesophageal echocardiogram, and anterior-cervical-decompression. Required time of typing for each of the above-mentioned reports calculated and compared with manual typing of the same words. It is revealed that 33.36% reduction in typing time and 73.53% reduction in keystroke. The designed system reduced the time of typing free text which might be an approach for EHRs improvement in terms of documentation.

Identifying and validating requirements of telemental health services for Iranian veterans

Background:

The ability of timely access to mental health care is very important for combat veterans that are facing many barriers such as living in rural and remote areas and the lack of integration. Telemental health services improve the veterans’ health situation by providing mental health care from a distance. We aimed to identify the telemental health service requirements for Iranian veterans and validate them from the perspective of the statistical population.

Methods:

This descriptive cross-sectional study was conducted in 2018. In the first phase, a review was conducted in relevant databases, such as PubMed, Scopus, Ovid, Ebsco, and Web of Science. In the second phase, veterans, mental health providers, and telemedicine experts were consulted to validating of the identified telemental health service requirements by a researcher-made questionnaire. Analysis of collecting data was done using SPSS software.

Results:

By full-text reviewing of 15 related articles, the identified elements were justified in 2 main categories and 24 subcategories including telemental health services (17 items) and telemental health requirements (7 items). According to the findings, the highest score was related to “save health-care costs” (4.47) and “reduce transportation-related problems” (4.47). Moreover, the “feasible alternative to face-to-face care” (2.22) obtained the lowest score from the perspective of the statistical population.

Conclusion:

Due to the importance of accessibility and patient-based mental health services, more studies are needed to investigate the point of views of patients and specialists to better understand the concerns and barriers to the implementation and use of telemental health services.

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.

International Comparison of Thalassemia Registries: Challenges and Opportunities

Background:

Patient registries use standardized methods to systematically gather uniform data for specific groups of patients managed in clinical practice to evaluate specified outcomes.

Aim:

The objective of this study was to identify and describe structures of the identified thalassemia registries in worldwide and summarize their key characteristics.

Methods:

We reviewed the literature on thalassemia registries. A search of PubMed, Scopus, ProQuest, and Science Direct databases was conducted in September 2018. We also reviewed the existing thalassemia registry websites in different countries. The keywords used to our search were as follows: Thalassemia, Hemoglobinopathy, Registry, Database, and Registration System. Some features such as the name of registry, funding source, objectives of the registry, minimum data set, and methods of data collection were determined.

Results:

We identified 16 thalassemia registries operating on a multinational, national, or regional level between1984 and 2016. Most of these aimed to improve the diagnosis and management of control programs. Government funding was the most common funding source for registries. Furthermore, the most common method of data submission was Web-based data entry. The data were entered by a member of the clinical team or a nominated data manager.

Conclusion:

Registries provide a positive return on investment; their establishment and maintenance require ongoing support by government, policy makers, research funding bodies, clinicians, thalassemia patients and their caregivers. However, the results of research suggest the establishment of an international network for coordination and collaboration between thalassemia registries.

A systematic literature review and classification of knowledge discovery in traditional medicine

INTRODUCTION AND OBJECTIVE

Despite the importance of machine learning methods application in traditional medicine there is a no systematic literature review and a classification for this field. This is the first comprehensive literature review of the application of data mining methods in traditional medicine.

METHOD

We reviewed 5 database between 2000 to 2017 based on the Kitchenham systematic review methodology. 502 articles were identified and reviewed for their relevance to application of machine learning methods in traditional medicine, 42 selected papers were classified and categorized on four dimension; 1) application domain of data mining techniques in traditional medicine; 2) the data mining methods most frequently used in traditional medicine; 3) main strength and limitation of data mining techniques in traditional medicine; 4) the performance evaluation methods in data mining methods in traditional medicine.

RESULT

The result obtained showed that main application domain of data mining techniques in traditional medicine was related to syndrome differentiation. Bayesian Networks (BNs), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) were recognized as being the methods most frequently applied in traditional medicine. Furthermore, each data mining techniques has its own strength and limitations when applied in traditional medicine. Single scaler methods were frequently used for performance evaluation of data mining methods.

CONCLUSION

Machine learning methods have become an important research field in traditional medicine. Our research provides information about this methods by examining the related articles.

The new roles of medical librarians in medical research

Purpose

This study aims to highlight the role of librarians as an essential element in medical research. For this purpose, the primary research process was divided into three phases: before, during and after. Then, the roles of librarians associated with each phase were separated and the viewpoint of researchers and librarians on the importance of these roles were considered and compared.

Design/methodology/approach

This comparative, descriptive-causal research was conducted using the census method. Birjand University, a type-2 university in the field of Medical Sciences according to the rating of the Iranian Ministry of Health and Medical Education, was selected for the study. The participants were all faculty members and all librarians working in the university’s libraries. The data collection tool was a questionnaire made by authors. Its validity was confirmed by four professors of Library Science, and questionnaire design expert and its reliability was confirmed with a Cronbach's alpha of 0.89. Data were analyzed with the Statistical Package for the Social Sciences, using descriptive statistics.

Findings

From the perspective of researchers, awareness-raising for open access resources, determination of standard subject keywords on the basis of Medical Subject Headings for articles and scientific texts and using scientific research findings as a basis for preventing duplicate studies in future research are the most important roles for librarians in the three stages of medical research. From the perspective of librarians, the use of knowledge management skills, searching scientific information as review of the literature and also selecting standard keywords to search the databases and providing health-care professionals with the findings of latest scientific research have the highest place in the different stages of the research lifecycle.

Originality/value

The difference between the viewpoints of librarians and researchers about the role of medical librarians at the various stages of the research lifecycle shows that there are significant gaps between the librarians’ services and users’ expectations. It is expected that through learning modern professional skills, medical librarians can assume new roles in medical research and make their capabilities known and available to researchers.