Prediction of subsequent fragility fractures: application of machine learning.

Background: Machine learning (ML) has shown exceptional promise in various domains of medical research. However, its application in predicting subsequent fragility fractures is still largely unknown. In this study, we aim to evaluate the predictive power of different ML algorithms in this area and identify key features associated with the risk of subsequent fragility fractures in osteoporotic patients.

Machine learning-based mortality prediction models for smoker COVID-19 patients.

Background: The large number of SARS-Cov-2 cases during the COVID-19 global pandemic has burdened healthcare systems and created a shortage of resources and services. In recent years, mortality prediction models have shown a potential in alleviating this issue; however, these models are susceptible to biases in specific subpopulations with different risks of mortality, such as patients with prior history of smoking. The current study aims to develop a machine learning-based mortality prediction model for COVID-19 patients that have a history of smoking in the Iranian population.

Establishment of Iran Musculoskeletal Tumor Registry: A Study Protocol and Lessons Learned from Implementation and the Pilot Phase.

Background: The number of available musculoskeletal tumor registries is relatively small. We developed a registry system focused on the clinical aspects of musculoskeletal tumors to improve quality of care indexes through the development of updated national protocols. In this study, we describe our protocol, challenges, and the data collected during the implementation of the registry system in a single-specialty orthopedic center in Iran.

The Persian Version of the Mobile Application Rating Scale (MARS-Fa): Translation and Validation Study.

Background: Approximately 110 million Farsi speakers worldwide have access to a growing mobile app market. Despite restrictions and international sanctions, Iran's internal mobile health app market is growing, especially for Android-based apps. However, there is a need for guidelines for developing health apps that meet international quality standards.

Telemedicine in the emergency department: an overview of systematic reviews.

Aim: There is both favorable and controversial evidence on the application of telemedicine in the emergency department (ED), which has created uncertainty regarding the effectiveness of these systems. We performed a systematic review of the literature on systematic reviews to provide an overview of the benefits and challenges to the application of telemedicine systems for the ED.

Subject And Methods: PubMed, Web of Science, Scopus, Cochrane Library, and Google Scholar databases were explored for systematic reviews of telemedicine applications for the ED.