National road traffic accident information system in Iran and selected countries.

Background: The National Road Traffic Accident Information System is crucial in enhancing road and traffic safety by providing managers and policymakers with systematic access to and analysis of crash data. Accordingly, the present study aims to review the data collection and exchange processes within these systems and to identify the roles and significance of the participating organizations.

Methods: The current study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Image Analysis as tool for Predicting Colorectal Cancer Molecular Alterations: A Scoping Review.

Objectives: Among the most important diagnostic indicators of colorectal cancer; however, measuring molecular alterations are invasive and expensive. This study aimed to investigate the application of image processing to predict molecular alterations in colorectal cancer.

Methods: In this scoping review, we searched for relevant literature by searching the Web of Science, Scopus, and PubMed databases.

Designing the Essential Informational Needs of a Smartphone Application for Self-Management of Patients With Inflammatory Bowel Disease.

Background And Aims: Inflammatory bowel disease (IBD) is a chronic digestive disease that has a detrimental effect on the quality of life of IBD patients. This study aims to identify the informational needs and design the essential informational needs for a smartphone application for the self-management of IBD.

Methods: This study was conducted in two stages and the informational needs of the patients were extracted in a questionnaire designed in three separate sections and given to 120 patients with UC and 60 patients with CD.

Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.

Background: Providing appropriate specialized treatment to the right patient at the right time is considered necessary in cancer management. Targeted therapy tailored to the genetic changes of each breast cancer patient is a desirable feature of precision oncology, which can not only reduce disease progression but also potentially increase patient survival. The use of artificial intelligence alongside precision oncology can help physicians by identifying and selecting more effective treatment factors for patients.

Non-invasive fractional flow reserve estimation using deep learning on intermediate left anterior descending coronary artery lesion angiography images.

This study aimed to design an end-to-end deep learning model for estimating the value of fractional flow reserve (FFR) using angiography images to classify left anterior descending (LAD) branch angiography images with average stenosis between 50 and 70% into two categories: FFR > 80 and FFR ≤ 80. In this study 3625 images were extracted from 41 patients' angiography films. Nine pre-trained convolutional neural networks (CNN), including DenseNet121, InceptionResNetV2, VGG16, VGG19, ResNet50V2, Xception, MobileNetV3Large, DenseNet201, and DenseNet169, were used to extract the features of images.

Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques.

Background: Fractional Flow Reserve (FFR) is the gold standard for the functional evaluation of coronary arteries, which is effective in selecting patients for revascularization, avoiding unnecessary procedures, and reducing treatment costs. However, its use is limited due to invasiveness, high cost, and complexity. Therefore, the non-invasive estimation of FFR using artificial intelligence (AI) methods is crucial.

Integrated personal health record (PHR) security: requirements and mechanisms.

Background: Personal Health Records (PHRs) are designed to fulfill the goals of electronic health (eHealth) and empower the individual in the process of self-care. Integrated PHR can improve the quality of care, strengthen the patient-healthcare provider relationship, and reduce healthcare costs. Still, the process of PHR acceptance and use has been slow and mainly hindered by people's concerns about the security of their personal health information.

Performance of machine learning techniques on prediction of esophageal varices grades among patients with cirrhosis.

Objectives: All patients with cirrhosis should be periodically examined for esophageal varices (EV), however, a large percentage of patients undergoing screening, do not have EV or have only mild EV and do not have high-risk characteristics. Therefore, developing a non-invasive method to predict the occurrence of EV in patients with liver cirrhosis as a non-invasive method with high accuracy seems useful. In the present research, we compared the performance of several machine learning (ML) methods to predict EV on laboratory and clinical data to choose the best model.

Identifying predictors of varices grading in patients with cirrhosis using ensemble learning.

Objectives: The present study was conducted to improve the performance of predictive methods by introducing the most important factors which have the highest effects on the prediction of esophageal varices (EV) grades among patients with cirrhosis.

Methods: In the present study, the ensemble learning methods, including Catboost and XGB classifier, were used to choose the most potent predictors of EV grades solely based on routine laboratory and clinical data, a dataset of 490 patients with cirrhosis gathered. To increase the validity of the results, a five-fold cross-validation method was applied.

Efficient Framework for Detection of COVID-19 Omicron and Delta Variants Based on Two Intelligent Phases of CNN Models.

Introduction: While the COVID-19 pandemic was waning in most parts of the world, a new wave of COVID-19 Omicron and Delta variants in Central Asia and the Middle East caused a devastating crisis and collapse of health-care systems. As the diagnostic methods for this COVID-19 variant became more complex, health-care centers faced a dramatic increase in patients. Thus, the need for less expensive and faster diagnostic methods led researchers and specialists to work on improving diagnostic testing.

Telemedicine Services in Chronic Obstructive Pulmonary Disease: A Systematic Review of Patients' Adherence.

Background: The current systematic review aimed to determine the effect of telemedicine services on adherence in patients with chronic obstructive pulmonary disease (COPD) and to describe the type of adherence and applied devices and modules.

Materials And Methods: We reviewed PubMed, Scopus, Web of Science, and Embase databases to identify relevant studies from the time of inception of these databases to March 10, 2019, using three groups of keywords. The first group comprised words describing COPD, the second group included words describing types of telemedicine interventions, and the third group contained words describing adherence.

Application of ICT in effective crisis management: A systematic review.

Introduction: Effective crisis management can reduce the costs and consequences of a crisis and has a significant impact on saving human lives in critical situations. Proper use of information and communication technologies (ICTs) can improve all crisis management phases and crisis communication cycles according to the needs of stakeholders. The purpose of this review article is to identify which ICTs have been used in effective crisis management and what managerial tasks they support.

Minimum Data Set for a Poisoning Registry: A Systematic Review.

Poisoning, as a well-known medical condition, puts everyone at risk. As a data management tool, a registry plays an important role in monitoring the poisoned patients. Having a poisoning minimum data set is a major requirement for creating a poisoning registry.

Functions and Outcomes of Personal Health Records for Patients with Chronic Diseases: A Systematic Review.

Introduction: The personal health record (PHR) makes it possible for patients to access, manage, track, and share their health information. By engaging patients in chronic disease care, they will be active members in decision-making and healthcare management.

Objectives: This study aimed to identify the functions and outcomes of PHR for patients with four major groups of chronic diseases (cardiovascular diseases, cancers, diabetes, and chronic respiratory diseases).

Development of a Knowledge-based Clinical Decision Support System for Multiple Sclerosis Diagnosis.

The diagnosis of multiple sclerosis (MS) is difficult considering its complexity, variety in signs and symptoms, and its similarity to the signs and symptoms of other neurological diseases. The purpose of this study is to design and develop a clinical decision support system (CDSS) to help physicians diagnose MS with a relapsing-remitting phenotype. The CDSS software was developed in four stages: requirement analysis, system design, system development, and system evaluation.

Notifiable Diseases Surveillance System with a Data Architecture Approach: a Systematic Review.

Introduction: The wide range of notifiable diseases and the need for immediate reporting complicate the management of these diseases. Developing a surveillance system using precise architectural principles could ease the management of these diseases.

Aim: The present study reviews the data architecture of notifiable diseases surveillance systems to provide a basis for developing such systems.

Intelligent Computer Systems for Multiple Sclerosis Diagnosis: a Systematic Review of Reasoning Techniques and Methods.

Objective: Intelligent computer systems are used in diagnosing Multiple Sclerosis and help physicians in the accurate and timely diagnosis of the disease. This study focuses on a review of different reasoning techniques and methods used in intelligent systems to diagnose MS and analyze the application and efficiency of different reasoning methods in order to find the most efficient and applicable methods and techniques for MS diagnosis.

Methods: A complete research was carried out on articles in various electronic databases based on Mesh vocabulary.

National information system for rare diseases with an approach to data architecture: A systematic review.

The study aims to systematically review literature on the rare diseases information system to identify architecture of this system from a data perspective. The search for relevant English language articles, based on keywords in title, abstract, Mesh and Emtree terms, was done in Pubmed and Embase (from 1980 to June 2017), Scopus, Science Direct and Cochran (from 1980 to July 2017). Articles were selected if they addressed data architecture of information systems with a focus on rare disease, and if at least one of their objectives dealt with design, implementation, and development of rare diseases information systems.

E-Health: a global approach with extensive semantic variation.

In recent years, there has been considerable attention towards the development of information and communication technology (ICT) in health care delivery known as 'E-Health'. The term "E-Health" is almost a new concept and the E-Health projects mainly aim to improve service delivery to people, though different countries might have different approaches in using E-Health. The focus of this study is to review factors influencing the development of E-Health projects, as these factors could lead to an extensive semantic variation.

A new model for the organizational structure of medical record departments in hospitals in Iran.

The organizational structure of medical record departments in Iran is not appropriate for the efficient management of healthcare information. In addition, there is no strong information management division to provide comprehensive information management services in hospitals in Iran. Therefore, a suggested model was designed based on four main axes: 1) specifications of a Health Information Management Division, 2) specifications of a Healthcare Information Management Department, 3) the functions of the Healthcare Information Management Department, and 4) the units of the Healthcare Information Management Department.