Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review.

During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML.

Public Speech Anxiety among Medical Residency Trainees in Riyadh.

Objective: The present study aimed at measuring the level of public speaking anxiety (PSA) among medical residents in Riyadh, in addition to identifying the factors influencing public speaking anxiety from the perspective of the medical residents.

Method: A cross-sectional survey was conducted over a sample of 203 medical residents in Riyadh. The study adopted the questionnaire as a data collection tool.

Medication Adherence Among Geriatric Patients with Chronic Diseases in Riyadh, Saudi Arabia.

Background: Medication non-adherence is a common and significant public health problem, especially among the geriatric population. This study's objective was to measure medication adherence and associated factors among geriatric patients with chronic diseases.

Methods: A cross-sectional study targeted outpatient geriatrics who suffer from chronic diseases at King Saud University Medical City (KSUMC), Riyadh, Saudi Arabia.