Race, Ethnicity, and Sex in Pediatric Eye Disease Investigator Group Clinical Studies.

Importance: Racial, ethnic, and sex disparities exist in US clinical study enrollment, and the prevalence of these disparities in Pediatric Eye Disease Investigator Group (PEDIG) clinical studies has not been thoroughly assessed. Objective: To evaluate racial, ethnic, and sex representation in PEDIG clinical studies compared with the 2010 US Census pediatric population. Design, Setting, and Participants: This cross-sectional analysis examined PEDIG clinical studies based in the US from December 1, 1997 to September 12, 2022, 41 of which met inclusion criteria of a completed study, a study population younger than 18 years, and 1 or more accompanying publication. Data analysis was performed between November 2023 and February 2024. Exposure: Study participant race, ethnicity, and sex for each clinical study, as collected from peer-reviewed publications, patient-enrollment datasets, and ClinicalTrials.gov. Main Outcomes and Measures: Median enrollment percentages of female, White, Black, Hispanic, Asian, and other race participants were calculated and compared with the 2010 US Census pediatric population using a 1-sample Wilcoxon rank test. Proportionate enrollment was defined as no difference on a 1-sample Wilcoxon rank test if P ≥ .05. If P < .05, we determined if the median enrollment percentage was greater than or less than 2010 US Census proportion to determine if enrollees were underrepresented or overrepresented. To calculate the magnitude of overrepresentation or underrepresentation, enrollment-census difference (ECD) was defined as the difference between groups' median enrollment percentage and percentage representation in the 2010 US Census. Compound annual growth rate (CAGR) was used to measure temporal trends in enrollment, and logistic regression analysis was used to analyze factors that may have contributed to proportionate representation outcomes. Results: A total of 11 658 study participants in 41 clinical studies were included; mean (SD) participant age was 5.9 (2.8) years and 5918 study participants (50.8%) were female. In clinical studies meeting inclusion criteria, White participants were overrepresented (ECD, 0.19; 95% CI, 0.10-0.28; P < .001). Black participants (ECD, -0.07; 95% CI, -0.10 to -0.03; P < .001), Asian participants (ECD, -0.03; 95% CI, -0.04 to -0.02; P < .001), and Hispanic participants (ECD, -0.09; 95% CI, -0.13 to -0.05; P < .001) were underrepresented. Female participants were represented proportionately (ECD, 0.004; 95% CI, -0.036 to 0.045; P = .21). White and Asian participants demonstrated a decreasing trend in study enrollment from 1997 to 2022 (White: CAGR, -1.5%; 95% CI, -2.3% to -0.6%; Asian: CAGR, -1.7%; 95% CI, -2.0% to -1.4%), while Hispanic participants demonstrated an increasing enrollment trend (CAGR, 7.2%; 95% CI, 3.7%-10.7%). Conclusions and Relevance: In this retrospective cross-sectional study of PEDIG clinical studies from December 1, 1997 to September 12, 2022, Black, Hispanic, and Asian participants were underrepresented, White participants were overrepresented, and female participants were represented proportionally. Trends suggested increasing enrollment of Hispanic participants and decreasing enrollment of White participants over time. This study demonstrates an opportunity to advocate for increased enrollment of underrepresented groups in pediatric ophthalmology clinical studies.

The impact of coronavirus pandemic (COVID-19) on education: The role of virtual and remote laboratories in education.

To avoid the spread of the COVID-19 crisis, many countries worldwide have temporarily shut down their academic organizations. National and international closures affect over 91% of the education community of the world. E-learning is the only effective manner for educational institutions to coordinate the learning process during the global lockdown and quarantine period. Many educational institutions have instructed their students through remote learning technologies to face the effect of local closures and promote the continuity of the education process. This study examines the expected benefits of e-learning during the COVID-19 pandemic by providing a new model to investigate this issue using a survey collected from the students at Imam Abdulrahman Bin Faisal University. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed on 179 useable responses. This study applied Push-Pull-Mooring theory and examined how push, pull, and mooring variables impact learners to switch to virtual and remote educational laboratories. The Protection Motivation theory was employed to explain how the potential health risk and environmental threat can influence the expected benefits from e-learning services. The findings revealed that the push factor (environmental threat) is significantly related to perceived benefits. The pull factors (e-learning motivation, perceived information sharing, and social distancing) significantly impact learners' benefits. The mooring factor, namely perceived security, significantly impacts learners' benefits.

A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique.

BACKGROUND: Hepatitis is an inflammation of the liver, most commonly caused by a viral infection. Supervised data mining techniques have been successful in hepatitis disease diagnosis through a set of datasets. Many methods have been developed by the aids of data mining techniques for hepatitis disease diagnosis. The majority of these methods are developed by single learning techniques. In addition, these methods do not support the ensemble learning of the data. Combining the outputs of several predictors can result in improved accuracy in classification problems. This study aims to propose an accurate method for the hepatitis disease diagnosis by taking the advantages of ensemble learning. METHODS: We use Non-linear Iterative Partial Least Squares to perform the data dimensionality reduction, Self-Organizing Map technique for clustering task and ensembles of Neuro-Fuzzy Inference System for predicting the hepatitis disease. We also use decision trees for the selection of most important features in the experimental dataset. We test our method on a real-world dataset and present our results in comparison with the latest results of previous studies. RESULTS: The results of our analyses on the dataset demonstrated that our method performance is superior to the Neural Network, ANFIS, K-Nearest Neighbors and Support Vector Machine. CONCLUSIONS: The method has potential to be used as an intelligent learning system for hepatitis disease diagnosis in the healthcare.

A soft computing approach for diabetes disease classification.

As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The aim of this study is to classify diabetes disease by developing an intelligence system using machine learning techniques. Our method is developed through clustering, noise removal and classification approaches. Accordingly, we use expectation maximization, principal component analysis and support vector machine for clustering, noise removal and classification tasks, respectively. We also develop the proposed method for incremental situation by applying the incremental principal component analysis and incremental support vector machine for incremental learning of data. Experimental results on Pima Indian Diabetes dataset show that proposed method remarkably improves the accuracy of prediction and reduces computation time in relation to the non-incremental approaches. The hybrid intelligent system can assist medical practitioners in the healthcare practice as a decision support system.

Prevalence of atrial fibrillation in the Malaysian communities.

BACKGROUND AND AIM: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia encountered in clinical practice. The REDISCOVER (Responding to Increasing Cardiovascular disease prevalence) study is an observational longitudinal community-based study that tracks changing lifestyles, risk factors and chronic disease in urban and rural areas of Malaysia. In this study, we aim to study the prevalence of AF and its associated risk factors. METHODS: The study was conducted between 2007 and 2014. Participants were required to complete questionnaires on cardiovascular risk factors and medical history, and undergo physical examinations, blood tests, ECG and echocardiography examinations. Demographic variables including weight, height, blood pressure, serum glucose and serum lipid were recorded. Participants with AF were identified from their baseline ECG and at 3-year follow up. RESULTS: A total of 10 805 subjects participated in the study. Mean age was 52.6(±11.6) years and 56% were female; 4.4% of subjects had a diagnosis of ischaemic heart disease, 1.3% had a previous stroke, 16.7% had diabetes mellitus and 45.6% had hypertension. There were 53 subjects diagnosed with AF at baseline, giving a prevalence of 0.49%, and 0.54% at 3 years. AF was more prevalent in males (58.5% in the AF group compared to 43.9% in sinus rhythm (SR) subjects; p=0.03) and the older age group. Ischaemic heart disease was more prevalent in AF subjects (22.6%) compared to SR subjects (4.4%) (p<0.001). In the AF group previous stroke had occurred in 1.9% of subjects compared to 1.3% in the SR population (p=0.51), and 24.5% of subjects in the AF group had diabetes compared to 16.6% in the SR group (p=0.12). There was a significant difference in the prevalence of hypertension between the AF group (59.6%) compared to the SR subjects (45.5%) (p=0.04). CONCLUSIONS: The prevalence of AF in the Malaysian population was low at 0.54% compared to the global average of 1%. We found that AF was associated with older age, male sex, hypertension, and ischaemic heart disease.

Accuracy Improvement for Predicting Parkinson's Disease Progression.

Parkinson's disease (PD) is a member of a larger group of neuromotor diseases marked by the progressive death of dopamineproducing cells in the brain. Providing computational tools for Parkinson disease using a set of data that contains medical information is very desirable for alleviating the symptoms that can help the amount of people who want to discover the risk of disease at an early stage. This paper proposes a new hybrid intelligent system for the prediction of PD progression using noise removal, clustering and prediction methods. Principal Component Analysis (PCA) and Expectation Maximization (EM) are respectively employed to address the multi-collinearity problems in the experimental datasets and clustering the data. We then apply Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) for prediction of PD progression. Experimental results on public Parkinson's datasets show that the proposed method remarkably improves the accuracy of prediction of PD progression. The hybrid intelligent system can assist medical practitioners in the healthcare practice for early detection of Parkinson disease.

Role of OpenEHR as an open source solution for the regional modelling of patient data in obstetrics.

This work investigates, whether openEHR with its reference model, archetypes and templates is suitable for the digital representation of demographic as well as clinical data. Moreover, it elaborates openEHR as a tool for modelling Hospital Information Systems on a regional level based on a national logical infrastructure. OpenEHR is a dual model approach developed for the modelling of Hospital Information Systems enabling semantic interoperability. A holistic solution to this represents the use of dual model based Electronic Healthcare Record systems. Modelling data in the field of obstetrics is a challenge, since different regions demand locally specific information for the process of treatment. Smaller health units in developing countries like Brazil or Malaysia, which until recently handled automatable processes like the storage of sensitive patient data in paper form, start organizational reconstruction processes. This archetype proof-of-concept investigation has tried out some elements of the openEHR methodology in cooperation with a health unit in Colombo, Brazil. Two legal forms provided by the Brazilian Ministry of Health have been analyzed and classified into demographic and clinical data. LinkEHR-Ed editor was used to read, edit and create archetypes. Results show that 33 clinical and demographic concepts, which are necessary to cover data demanded by the Unified National Health System, were identified. Out of the concepts 61% were reused and 39% modified to cover domain requirements. The detailed process of reuse, modification and creation of archetypes is shown. We conclude that, although a major part of demographic and clinical patient data were already represented by existing archetypes, a significant part required major modifications. In this study openEHR proved to be a highly suitable tool in the modelling of complex health data. In combination with LinkEHR-Ed software it offers user-friendly and highly applicable tools, although the complexity built by the vast specifications requires expert networks to define generally excepted clinical models. Finally, this project has pointed out main benefits enclosing high coverage of obstetrics data on the Clinical Knowledge Manager, simple modelling, and wide network and support using openEHR. Moreover, barriers described are enclosing the allocation of clinical content to respective archetypes, as well as stagnant adaption of changes on the Clinical Knowledge Manager leading to redundant efforts in data contribution that need to be addressed in future works.

Organizational decision to adopt hospital information system: an empirical investigation in the case of Malaysian public hospitals.

OBJECTIVES: This study mainly integrates the mature Technology-Organization-Environment (TOE) framework and recently developed Human-Organization-Technology (HOT) fit model to identify factors that affect the hospital decision in adopting Hospital Information System (HIS). METHODS: Accordingly, a hybrid Multi-Criteria-Decision-Making (MCDM) model is used to address the dependence relationships of factors with the aid of Analytic Network Processes (ANP) and Decision Making Trial and Evaluation Laboratory (DEMATEL) approaches. The initial model of the study is designed by considering four main dimensions with 13 variables as organizational innovation adoption factors with respect to HIS. By using DEMATEL, the interdependencies strength among the dimensions and variables are tested. The ANP method is then adopted in order to determine the relative importance of the adoption factors, and is used to identify how these factors are weighted and prioritized by the public hospital professionals, who are wholly familiar with the HIS and have years of experience in decision making in hospitals' Information System (IS) department. RESULTS: The results of this study indicate that from the experts' viewpoint "Perceived Technical Competence" is the most important factor in the Human dimension. In the Technology dimension, the experts agree that the "Relative Advantage" is more important in relation to the other factors. In the Organization dimension, "Hospital Size" is considered more important rather than others. And, in the Environment dimension, according to the experts judgment, "Government Policy" is the most important factor. The results of ANP survey from experts also reveal that the experts in the HIS field believed that these factors should not be overlooked by managers of hospitals and the adoption of HIS is more related to more consideration of these factors. In addition, from the results, it is found that the experts are more concerned about Environment and Technology for the adoption HIS. CONCLUSIONS: The findings of this study make a novel contribution in the context of healthcare industry that is to improve the decision process of innovation in adoption stage and to help enhance more the diffusion of IS in the hospital setting, which by doing so, can provide plenty of profits to the patient community and the hospitals.