ABSTRACT
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. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.
ABSTRACT
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. Telephone interviews were utilized to collect data from participants using a structured questionnaire and the GMAS validated instrument scale (General Medication Adherence Scale) intended to measure important determinants impacting adherence: patient behaviour, cost, comorbidity, and pill burden. Results: A total of 422 patients were assessed for medication adherence. The Mean overall score for GMAS was 29.9±3.1 out of 33. (64.9%) of the patients had a high level of medication adherence. The patients had a high adherence on the domain of patient behavior related non-adherence (PBNA) (13.5±1.9) out of 15, a high adherence on the domain of additional disease and pill burden (ADPB) (11.2±1.4) out of 12, and good to high adherence on the cost-related non-adherence (CRNA) (5.25±1.1) out of 6. Conclusion: The geriatric population with chronic diseases in our study had a good level of adherence to medication if compared with other international figures. To promote better medication adherence, patients must have a good understanding of their disease and strong beliefs about the medications prescribed.
