Objective To understand the influence of demographics and education levels on awareness levels, and on the prevalence of hesitancy to receive the influenza vaccine among adult patients at King Saud University Medical City (KSUMC). Method A crosssectional study in the outpatient pharmacy area at KSUMC was conducted. Data was collected from January 1 to January 31, 2020. A total of 318 random adult patients were encountered and a predesigned survey was administered. After capturing demographic information, respondents were categorized into 3 groups: group A consisted of respondents who had never heard of the influenza vaccine; group B was comprised of respondents who answered that they had never received the influenza vaccine; and group C included respondents who answered that they had received at least one influenza vaccine. Results Out of the 317 survey respondents, 36 (11%) had never heard of the influenza vaccine (Group A). Of the remaining 281 (89%), 122 (39%) had not received the vaccine (Group B), whereas 159 (50%) had received it (Group C). Chi-square test results indicated a significant association between age group and awareness of the vaccine (p = .023). Moreover, there was a significant association between education level and awareness of the vaccine (p = .002). The prevalence of vaccination hesitancy was 42%. Chi-square test results indicated a significant association between gender and vaccination hesitancy (p < .001), and between education level and vaccination hesitancy (p = .011). Conclusion Influenza vaccination hesitancy is prevalent among the study's population. Further efforts by health care providers and public health services may be necessary to educate the community regarding the influenza vaccine's safety and efficacy.
Background: Hepatocellular carcinoma (HCC) is considered the most common type of liver cancer and the fourth leading cause of cancer-related deaths in the world. Since the disease is usually diagnosed at advanced stages, it has poor prognosis. Therefore, reliable biomarkers are urgently needed for early diagnosis and prognostic assessment.Methods: We used genome-wide gene expression profiling datasets from human and rat early HCC (eHCC) samples to perform integrated genomic and network-based analyses, and discovered gene markers that are expressed in blood and conserved in both species. We then used independent gene expression profiling datasets for peripheral blood mononuclear cells (PBMCs) for eHCC patients and from The Cancer Genome Atlas (TCGA) database to estimate the diagnostic and prognostic performance of the identified gene signature. Furthermore, we performed functional enrichment, interaction networks and pathway analyses.Results: We identified 41 significant genes that are expressed in blood and conserved across species in eHCC. We used comprehensive clinical data from over 600 patients with HCC to verify the diagnostic and prognostic value of 41-gene-signature. We developed a prognostic model and a risk score using the 41-geneset that showed that a high prognostic index is linked to a worse disease outcome. Furthermore, our 41-gene signature predicted disease outcome independently of other clinical factors in multivariate regression analysis. Our data reveals a number of cancer-related pathways and hub genes, including EIF4E, H2AFX, CREB1, GSK3B, TGFBR1, and CCNA2, that may be essential for eHCC progression and confirm our gene signature’s ability to detect the disease in its early stages in patients’ biological fluids instead of invasive procedures and its prognostic potential.Conclusion: Our findings indicate that integrated cross-species genomic and network analysis may provide reliable markers that are associated with eHCC that may lead to better diagnosis, prognosis, and treatment options.
BACKGROUND As opioid prescriptions have risen, there has also been a rise in opioid overdose deaths and substance use disorders. Public health systems have tried to improve their ability to detect and intervene in opioid use disorders to prevent death due to overdose. OBJECTIVE The objective of this study is to compare two approaches to identify opioid use problems (OUP) using electronic health record data- text mining versus diagnostic codes. METHODS Our sample consisted of adults on long-term opioid therapy (LTOT), defined as at least ≥ 70 days of supply within 90 days, and who visited a large multi-hospital network within a two-year period, between 1 January 2013 and 31 December 2014. We excluded patients with active cancer or schizophrenia. Text mining results were validated by a semi-assisted human review process and positive predictive value and level of agreement was reported. Each algorithm sought to identify patients who visited a health care facility due to an opioid poisoning event, opioid abuse, or opioid dependence. Population characteristics for positive OUP identified by text mining and ICD cohorts were compared. Chi-square and Fishers exact test were used for categorical data analysis and independent t-test was used to compare means for continuous variables. We further compared the demographics of the cohorts identified by the two methods. RESULTS We identified 14,298 eligible LTOT patients. Text mining of relevant electronic clinical notes yielded 127 positive OUP cases compared to 45 cases using International Classification of Disease (ICD)-9 codes for the same population. Just eight OUP patients were identified using both methods. The two cohorts differed significantly with respect to age, gender, and other characteristics CONCLUSIONS Compared to diagnostic codes, text mining identified more OUP cases with distinct characteristics. Incorporating text-mining techniques into OUP surveillance methods may support better detection of OUP and more accurate estimates of prevalence. CLINICALTRIAL NA
Background: Artificial intelligence (AI) is becoming an essential tool in the disability world. AI can be applied in a variety of contexts and domains, ranging from assistive devices for disabled individuals to decision-making systems that aid healthcare providers in the diagnosis and treatment of disabilities. Examples of these contexts include discrimination of behaviors, identifying and assessing disability-related problems, detecting abnormalities, classifying disabling diseases, predicting the progression of disabilities, and assisting and triaging patients.Methods: We investigated which AI predictive analytics models and features have been used in disability research locally and globally, focusing on multiple sclerosis. This allowed us to test multiple algorithms to determine which was best suited for each kind of data available locally. Results: We developed a framework called Artificial Intelligence Group for Disability Research (AIGDR) for disability-related research at King Salman Centre for Disability Research. The framework was influenced by existing AI use in disability research. Conclusions: The AIGDR framework uses existing predictive analytics models to generate dashboards and reports for decision-makers and produce a database that can help researchers conduct research. In the future, we plan to develop the proposed platform and apply it in various disability contexts in Saudi Arabia.
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