Deep Learning-Based Prediction of Daily COVID-19 Cases Using X (Twitter) Data.

Due to the importance of COVID-19 control, innovative methods for predicting cases using social network data are increasingly under attention. This study aims to predict confirmed COVID-19 cases using X (Twitter) social network data (tweets) and deep learning methods. We prepare data extracted from tweets by natural language processing (NLP) and consider the daily G-value (growth rate) as the target variable of COVID-19, collected from the worldometer.

Unobtrusive Monitoring of Clinical Deterioration in Smart Homes.

Clinical deterioration (CD) is the physiological decompensation that incurs care escalation, protracted hospital stays, or even death. The early warning score (EWS) calculates the occurrence of CD based on five vital signs. However, there are limited reports regarding EWS monitoring in smart home settings.

Practical Evaluation of ChatGPT Performance for Radiology Report Generation.

Rationale And Objectives: The process of generating radiology reports is often time-consuming and labor-intensive, prone to incompleteness, heterogeneity, and errors. By employing natural language processing (NLP)-based techniques, this study explores the potential for enhancing the efficiency of radiology report generation through the remarkable capabilities of ChatGPT (Generative Pre-training Transformer), a prominent large language model (LLM).

Materials And Methods: Using a sample of 1000 records from the Medical Information Mart for Intensive Care (MIMIC) Chest X-ray Database, this investigation employed Claude.

Nutritional management recommendation systems in polycystic ovary syndrome: a systematic review.

Background: People with polycystic ovary syndrome suffer from many symptoms and are at risk of developing diseases such as hypertension and diabetes in the future. Therefore, the importance of self-care doubles. It is mainly to modify the lifestyle, especially following the principles of healthy eating.

A Mixed Model Approach for Estimating the Optimal Food Fortification of Vitamin D: Experiment Based on Mashhad Cohort Study in Iran.

Background: Vitamin D deficiency is a prevalent problem in worldwide healthcare related to several system disorders. Food fortification as a solution is associated with several challenges including insufficient coverage of the entire population, required degree of fortification, the vehicles used for fortification and potential toxicity. This study aimed to determine the optimal amount of vitamin D for fortification without surpassing the upper intake level (UL) of intake at the 95th percentile of the Iranian population and compare two methods of food fortification.

The effect of personalized intelligent digital systems for self-care training on type II diabetes: a systematic review and meta-analysis of clinical trials.

Aims: Type 2 diabetes (T2D) is rising worldwide. Self-care prevents diabetic complications. Lack of knowledge is one reason patients fail at self-care.

The Minimum Feature Set for Designing Mobile Apps to Support Bipolar Disorder-Affected Patients: Proposal of Essential Functions and Requirements.

Research conducted on mobile apps providing mental health services has concluded that patients with mental disorders tend to use such apps to maintain mental health balance technology may help manage and monitor issues like bipolar disorder (BP). This study was conducted in four steps to identify the features of designing a mobile application for BP-affected patients including (1) a literature search, (2) analyzing existing mobile apps to examine their efficiency, (3) interviewing patients affected with BP to discover their needs, and 4) exploring the points of view of experts using a dynamic narrative survey. Literature search and mobile app analysis resulted in 45 features, which were later reduced to 30 after the experts were surveyed about the project.

Developing a mobile health application for wound telemonitoring: a pilot study on abdominal surgeries post-discharge care.

Background: Many early signs of Surgical Site Infection (SSI) developed during the first thirty days after discharge remain inadequately recognized by patients. Hence, it is important to use interactive technologies for patient support in these times. It helps to diminish unnecessary exposure and in-person outpatient visits.

Data Sharing Platform for MIMIC-IV and MIMIC-ED Data Marts: Designing a Data Retrieving System Based on the Intra-Hospital Patient Transfer Pathway.

Accessibility to high-quality historical data for patients in hospitals may facilitate related predictive model development and data analysis experiments. This study provides a design for a data-sharing platform based on all possible criteria for Medical Information Mart for Intensive Care (MIMIC) IV and Emergency MIMIC-ED. Tables containing columns of medical attributions and outcomes were studied by a team of 5 experts in Medical Informatics.

A protocol for a systematic review of electronic early warning/track-and-trigger systems (EW/TTS) to predict clinical deterioration: Focus on automated features, technologies, and algorithms.

Background: This is a systematic review protocol to identify automated features, applied technologies, and algorithms in the electronic early warning/track and triage system (EW/TTS) developed to predict clinical deterioration (CD).

Methodology: This study will be conducted using PubMed, Scopus, and Web of Science databases to evaluate the features of EW/TTS in terms of their automated features, technologies, and algorithms. To this end, we will include any English articles reporting an EW/TTS without time limitation.

The effect of m-health applications on self-care improvement in older adults: A systematic review.

Participants: Four electronic databases were searched on March 6, 2020 including Scopus, PubMed, ISI, and Embase.

Methods: Our search consisted of concepts of "self-care," "elderly" and "Mobile device." English journal papers and, RCTs conducted for individuals older than 60 in the last 10 years were included.

Remote Monitoring of Colorectal Cancer Survivors Using a Smartphone App and Internet of Things-Based Device: Development and Usability Study.

Background: Patients with colorectal cancer who undergo surgery face many postoperative problems. These problems include the risk of relapse, side effects, and long-term complications.

Objective: This study sought to design and develop a remote monitoring system as a technological solution for the postdischarge care of these patients.

Artificial Intelligence Approaches on X-ray-oriented Images Process for Early Detection of COVID-19.

Background: COVID-19 is a global public health problem that is crucially important to be diagnosed in the early stages. This study aimed to investigate the use of artificial intelligence (AI) to process X-ray-oriented images to diagnose COVID-19 disease.

Methods: A systematic search was conducted in Medline (through PubMed), Scopus, ISI Web of Science, Cochrane Library, and IEEE Xplore Digital Library to identify relevant studies published until 21 September 2020.

Requirement Analysis for an Intelligent Warning System to Alarm the Rapid Response Team Prior to Patient Deterioration.

Unlabelled: The early warning system alarms the rapid response team (RRT) for clinical deterioration monitoring and prediction. Available systems do not perform well to decrease the number of ICU transfers or death. This study aimed to address the requirement of an intelligent warning system for timely and accurate RRT activation.

Early Warning System for Emergency Care: Designing a Timely Monitoring Mobile-Based System.

Early Warning Scores (EWSs) systems support the timely detection of patient deterioration and rapid response of the care team. Due to the mobility nature of healthcare settings, there has been a growing tendency to use mobile-based devices in these settings. This chapter aimed to design a mobile-based EWS application (app).

Towards the Application of Machine Learning in Emergency Informatics.

Emergency care is one of the cornerstone parts of the world health organization's action plan. Rapid response and immediate care are considered in agile emergency care. Artificial intelligence (AI) and informatics have been applied to fulfill these requirements through automated emergency technology.

Enhanced childhood diseases treatment using computational models: Systematic review of intelligent experiments heading to precision medicine.

Introduction: Precision or personalized Medicine (PM) is used for the prevention and treatment of diseases by considering a huge amount of information about individuals variables. Due to high volume of information, AI-based computational models are required. A large set of studies conducted to examine the PM approach to improve childhood clinical outcomes.

Nursing process education: A review of methods and characteristics.

The concepts of the nursing process have been assigned as important plans for the curricula and continuing education programs in the nursing discipline. This paper focuses on the main characteristics of the educational methods applied in training programs in the nursing process or nursing care plan. The original studies were extracted from the PubMed, ProQuest, Scopus, Science Direct and, Web of Science databases, following two-step screening by two reviewers.

A study of factors related to patients' length of stay using data mining techniques in a general hospital in southern Iran.

Purpose: The length of stay (LOS) in hospitals is a widely used indicator for goals such as health care management, quality control, utilizing hospital services and resources, and determining the degree of efficiency. Various methods have been used to identify the factors influencing the LOS. This study adopts a comparative approach of data mining techniques for investigating effective factors and predict the length of stay in Shahid-Mohammadi Hospital, Bandar Abbas, Iran.

Data Requirements for Information Management System Development for Poisoning with Acidic and Alkaline Substances.

Introduction: Ingestion of acidic or alkaline substances and its long-term effects on digestive system indicates is a common health problem worldwide. To identify the root causes of injuries, standard data collection is required.

Aim: The present study was conducted to determine the data requirements for the establishment of information management system for poisoning with acidic and alkaline substances in Iran.

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.

Occupational Disease Registries-Characteristics and Experiences.

Introduction: Due to growth of occupational diseases and also increase of public awareness about their consequences, attention to various aspects of diseases and improve occupational health and safety has found great importance. Therefore, there is the need for appropriate information management tools such as registries in order to recognitions of diseases patterns and then making decision about prevention, early detection and treatment of them. These registries have different characteristics in various countries according to their occupational health priorities.

Systematic Review of Hospital Based Cancer Registries (HBCRs): Necessary Tool to Improve Quality of Care in Cancer Patients.

Introduction: Incidence and mortality rate of cancer is increasing in all countries including low and middle-income countries. Hospital based cancer registry (HBCR) is an important tool for administration purpose and improvement of the quality of care. It is also important resource for population based cancer registries.

Prevalence of and risk factors for multidrug-resistant tuberculosis in Iran and its neighboring countries: systematic review and meta-analysis.

Tuberculosis, in particular drug-resistant tuberculosis, is of global concern due to the high mortality and morbidity associated with it annually. The aim of this study was to determine the prevalence of and the risk factors for multidrug-resistant tuberculosis in Iran and its neighboring countries. Four databases (PubMed, BioMed Central, EMBASE, and Web of Science) were searched using key terms.