Zero Trust Architecture: Trend and Impacton Information Security

Traditional-based security models are a threat to information security; they have been regarded as weak and ineffective to meet the dynamics of information system trust. An emerging framework, Zero Trust Architecture (ZTA) seeks to close the trust gap in information security through enforcing policies based on identity and continuous authentication and verification. This framework is built on several trust nodes and logical components that attempt to close the trust gap that exists in an information system. The adoption of this framework is still in its teething stage which is a result of several misleading deductions and assumptions. We attempt to explore the intricacies in the framework and close the existing knowledge gap. we surveyed the literature on ZTA and provided a foundational discussion on its implementation and effectiveness from prior studies. while we do not critique other models, this paper studied the strength and variables of the zero-trust security architecture and attempt to provide an overview of the model and close the knowledge gap on the effectiveness of adopting a Zero trust philosophy

A Comparison of Univariate and Multivariate Forecasting Models Predicting Emergency Department Patient Arrivals during the COVID-19 Pandemic

The COVID-19 pandemic has heightened the existing concern about the uncertainty surrounding patient arrival and the overutilization of resources in emergency departments (EDs). The prediction of variations in patient arrivals is vital for managing limited healthcare resources and facilitating data-driven resource planning. The objective of this study was to forecast ED patient arrivals during a pandemic over different time horizons. A secondary objective was to compare the performance of different forecasting models in predicting ED patient arrivals. We included all ED patient encounters at an urban teaching hospital between January 2019 and December 2020. We divided the data into training and testing datasets and applied univariate and multivariable forecasting models to predict daily ED visits. The influence of COVID-19 lockdown and climatic factors were included in the multivariable models. The model evaluation consisted of the root mean square error (RMSE) and mean absolute error (MAE) over different forecasting horizons. Our exploratory analysis illustrated that monthly and weekly patterns impact daily demand for care. The Holt–Winters approach outperformed all other univariate and multivariable forecasting models for short-term predictions, while the Long Short-Term Memory approach performed best in extended predictions. The developed forecasting models are able to accurately predict ED patient arrivals and peaks during a surge when tested on two years of data from a high-volume urban ED. These short-and long-term prediction models can potentially enhance ED and hospital resource planning

Assessing the state of rainwater for consumption in a community in dire need of clean water: Human and health risk using HERisk

This study examines the case of Ekpoma community, Edo State, Nigeria, where roof-harvested rainwater is the primary source of water for drinking and domestic purposes. Eight potentially toxic elements (PTEs), namely aluminum, chromium, copper, iron, manganese, nickel, lead, and zinc, were detected in rainwater samples, collected and analyzed from 54 sampling locations across the community. The elemental concentrations were quantified using atomic absorption spectrophotometry and compared using the regulatory standards of the World Health Organization, United States Environmental Protection Agency, and Nigerian Drinking Water Quality Standards. The PTEs detected in the rainwater samples can be attributed to the nature of the materials used in the roof catchment systems, storage tank conditions, anthropogenic effects from industrial and agricultural processes, and fossil fuel emissions. However, only 20% of the evaluated samples contained PTE concentrations below the allowable regulatory limits. Spatio-temporal health risk analysis conducted using HERisk software showed that children in the development phase (1–18 years) are most vulnerable to health risks in the community. After age 18, the risk increased by approximately 10% and remained constant until old age. In addition, the evaluation of the studied sites showed that 33% of the evaluated sites had negligible carcinogenic risks, while the other 61% were sites with low carcinogenic risks to residents

Failure Type Prediction In Software-Related Medical Device Recalls

With the existence of cutting-edge technology and research and development present in the world today, there is elevated expectations that defective product could be minimized, if not eliminated. This has dramatically led to the inevitability of product recall in this era. The motivation of this research stems from an increase in software failure recalls throughout the years, with in-vitro diagnostics and imaging as the two main categories of software medical device recall, which results in faults going undetected before getting in contact with the end-user, increase in the number of injuries and death and increase in financial losses accrued by the recalling firm. In this study, a framework comprising of text mining, latent semantic analysis and classification algorithms that predict the failure type experienced by the above-mentioned devices via the application of machine learning algorithms is developed by using the Food and Drug Administration Weekly Enforcement Report dataset. Four popular machine learning algorithms, the percentage-split method, and seven classifiers performance evaluation metrics such as classification accuracy, specificity, sensitivity, Matthews’ correlation coefficient, and execution time is used. The framework can easily identify and classify devices with control flow faults from those with integration fault. Furthermore, receiver optimistic curves and area under the curves for each classifier is computed. The performance of the proposed system has been validated on full features, with an 80% on the training set, and the remaining 20% on the test set. The framework presented in this paper would act as a machine-learning-based decision support system that will assist the medical device manufacturers to detect medical devices with faults efficiently

Acute Coronary Syndrome Prediction: A Data-Driven Machine Learning Modeling Approach In Emergency Care

Healthcare facilities are faced with significant challenges all year round, with patients presenting to the emergency department (ED) with different health issues. Of these challenges, heart disease seems to be an outlier. With heart disease being the primary cause of mortality and morbidity in both developed and developing countries, clinical concerns for acute coronary syndrome (ACS) are one of emergency medicine’s most common patient encounters. Of the three sub-categories of ACS, non-ST-segment elevation myocardial infarction (NSTEMI) has a long-term impact on the well-being of patients if left untreated. Previous efforts in hospital management have applied machine learning algorithms in differentiating NSTEMI from unstable angina (UA), another sub-category of ACS.Nonetheless, currently, we are faced with people who present to the ED with other clinical concerns for ACS but do not necessarily have ACS. No research has been done to differentiate NSTEMI from UA and other non-ACS etiologies. This research work aims to develop models that aid in answering these research questions: (a) How can we effectively develop a decision support tool to classify NSTEMI patients with clinical concerns for ACS? (b) What effect do the clinical narratives have on classifying NSTEMI patients with clinical concerns for ACS? (c) How can we model to minimize the length of stay for NSTEMI patients in the ED subject to controllable parameters? We propose an ensemble learning-driven simulation-optimization framework to help in addressing these questions following three phases. Phase one – developing a unique multi-class classification algorithm to identify risk factors that allow physicians to rule out NSTEMI patients and effectively classify them with the proper evaluation metrics. Phase two – building a multi-class classification framework that incorporates clinical narratives from the physicians’ comments and the clinical data to see whether it enhances the classification power of our model. Phase three – developing a simulation-optimization framework to investigate how resource allocation affects the ED’s performance for NSTEMI patients. The expected outcomes of the study are the combination of risk factors to help physicians rule out NSTEMI patients, thereby enhancing the performance of the ED and studying the interactions between the different ED resources to improve the quality of service for NSTEMI patients. Our proposed framework will assist healthcare administrators, and physicians plan effectively to address issues with NSTEMI patients instead of just sticking to the status quo

Fintech adoption dynamics in a pandemic: An experience from some financial institutions in Nigeria during COVID-19 using machine learning approach

AbstractThe novel coronavirus caused a lifestyle shift, and the acceptance of offsite financial transactions is still a case for financial technology (fintech). Mobile financial transactions continue to be at an all-time low, and financial institutions are developing approaches for financial digitalization acceptability. The present study attempts to understand users’ motivations for fintech adoption. The technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTUAT) were utilized to uncover the rationale behind technology adoption. This study explored the drivers inhibiting the adoption of financial technology in Nigeria during the pandemic. A machine learning (ML) approach was implemented to examine fintech adoption predictors using a self-administered consumer survey of 480 account holders. Survey responses were analyzed using a set of ML models (naïve Bayes, logistic regression, K-nearest neighbors, decision trees, and support vector machines), revealing the features and decision criteria for predicting perceived technology adoption. The decision tree outperformed the other models, with an accuracy of over 84%, precision of 88%, recall of 86%, F1-score of 84%, and area under the curve of 87%. The result indicates that customers are concerned about their safety. Thus, furthering their sense of risk. These results provide a roadmap for financial institutions and policymakers to understand behavioral attitudes toward adopting fintech and suggest strategies for attracting customers to the fintech space

Acute coronary syndrome prediction in emergency care: A machine learning approach

BACKGROUND AND OBJECTIVE: Clinical concern for acute coronary syndrome (ACS) is one of emergency medicine\u27s most common patient encounters. This study aims to develop an ensemble learning-driven framework as a diagnostic support tool to prevent misdiagnosis. METHODS: We obtained extensive clinical electronic health data on patient encounters with clinical concerns for ACS from a large urban emergency department (ED) between January 2017 and August 2020. We applied an analytical framework equipped with many well-developed algorithms to improve the data quality by addressing missing values, dimensionality reduction, and data imbalance. We trained ensemble learning algorithms to classify patients with ACS or non-ACS etiologies of their symptoms. We used performance evaluation metrics such as accuracy, sensitivity, precision, F1-score, and the area under the receiver operating characteristic (AUROC) to measure the model\u27s performance. RESULTS: The analysis included 31,228 patients, of whom 563 (1.8%) had ACS and 30,665 (98.2%) had alternative diagnoses. Eleven features, including systolic blood pressure, brain natriuretic peptide, chronic heart disease, coronary artery disease, creatinine, glucose, heart attack, heart rate, nephrotic syndrome, red cell distribution width, and troponin level, are reported as significantly contributing risk factors. The proposed framework successfully classifies these cohorts with sensitivity and AUROC as high as 86.3% and 93.3%. Our proposed model\u27s accuracy, precision, specificity, Matthew\u27s correlation coefficient, and F1-score were 85.7%, 86.3%, 93%, 80%, and 86.3%, respectively. CONCLUSION: Our proposed framework can identify early patients with ACS through further refinement and validation

A hybrid machine learning and natural language processing model for early detection of acute coronary syndrome

Acute coronary syndrome (ACS) is a leading cause of mortality and morbidity. Predicting the associated risks of patients with chest pain using electronic health record data can help identify those needing more tailored care. This study proposes the development of a reliable prediction framework to serve as a diagnostic support tool for preventing misdiagnoses among patients with clinical concerns for ACS. Data were collected from an urban, demographically diverse hospital in Detroit, Michigan, for patients presenting to the emergency department (ED) with primary chief complaints of chest pain from January 2017 to August 2020. This study incorporated term frequency-inverse document frequency features from free-text summaries, which contain anecdotal symptom descriptions and are among the first data points provided upon entering the ED. The analysis included 16,096 patients with clinical concerns for ACS and trained three machine learning models, logistic regression, AdaBoost, and linear discriminant analysis, across different data processing stages to predict patients with ACS from non-ACS etiology. The AdaBoost model outperformed the other two models with an accuracy of 94% and an F1-score of 0.943 in predicting ACS on the testing data. This study identified key independent factors from patient demographics, comorbidities, and clinical narrative data that predicted ACS in patients. The prediction framework can serve as a decision-support tool to classify ACS and inform physicians about better ACS risk factors