The World Health Organization labelled the new COVID-19 breakout a public health crisis of worldwide concern on 30 January 2020, and it was named the new global pandemic in March 2020. It has had catastrophic consequences on the world economy and well-being of people and has put a tremendous strain on already-scarce healthcare systems globally, particularly in underdeveloped countries. Over 11 billion vaccine doses have already been administered worldwide, and the benefits of these vaccinations will take some time to appear. Today, the only practical approach to diagnosing COVID-19 is through the RT-PCR and RAT tests, which have sometimes been known to give unreliable results. Timely diagnosis and implementation of precautionary measures will likely improve the survival outcome and decrease the fatality rates. In this study, we propose an innovative way to predict COVID-19 with the help of alternative non-clinical methods such as supervised machine learning models to identify the patients at risk based on their characteristic parameters and underlying comorbidities. Medical records of patients from Mexico admitted between 23 January 2020 and 26 March 2022, were chosen for this purpose. Among several supervised machine learning approaches tested, the XGBoost model achieved the best results with an accuracy of 92%. It is an easy, non-invasive, inexpensive, instant and accurate way of forecasting those at risk of contracting the virus. However, it is pretty early to deduce that this method can be used as an alternative in the clinical diagnosis of coronavirus cases.

Knee osteoarthritis is one of the most prevalent chronic diseases. It leads to pain, stiffness, decreased participation in activities of daily living and problems with balance recognition. Force platforms have been one of the tools used to analyse balance in patients. However, identification in early stages and assessing the severity of osteoarthritis using parameters derived from a force plate are yet unexplored to the best of our knowledge. Combining artificial intelligence with medical knowledge can provide a faster and more accurate diagnosis. The aim of our study is to present a novel algorithm to classify the occurrence and severity of knee osteoarthritis based on the parameters derived from a force plate. Forty-four sway movements graphs were measured. The different machine learning algorithms, such as K-Nearest Neighbours, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Decision Tree Classifier and Random Forest Classifier, were implemented on the dataset. The proposed method achieves 91% accuracy in detecting sway variation and would help the rehabilitation specialist to objectively identify the patient’s condition in the initial stage and educate the patient about disease progression.

Physical and cloud storage services are well-served by functioning and reliable high-volume storage systems. Recent observations point to hard disk reliability as one of the most pressing reliability issues in data centers containing massive volumes of storage devices such as HDDs. In this regard, early detection of impending failure at the disk level aids in reducing system downtime and reduces operational loss making proactive health monitoring a priority for AIOps in such settings. In this work, we introduce methods of extracting meaningful attributes associated with operational failure and of pre-processing the highly imbalanced health statistics data for subsequent prediction tasks using data-driven approaches. We use a Bidirectional LSTM with a multi-day look back period to learn the temporal progression of health indicators and baseline them against vanilla LSTM and Random Forest models to come up with several key metrics that establish the usefulness of and superiority of our model under some tightly defined operational constraints. For example, using a 15 day look back period, our approach can predict the occurrence of disk failure with an accuracy of 96.4% considering test data 60 days before failure. This helps to alert operations maintenance well in-advance about potential mitigation needs. In addition, our model reports a mean absolute error of 0.12 for predicting failure up to 60 days in advance, placing it among the state-of-the-art in recent literature.

Productivity losses caused by absenteeism at work cost U.S. employers billions of dollars each year. In addition, employers typically spend a considerable amount of time managing employees who perform poorly. By using predictive analytics and machine learning algorithms, organizations can make better decisions, thereby increasing organizational productivity, reducing costs, and improving efficiency. Thus, in this paper we propose hybrid optimization methods in order to find the most parsimonious model for absenteeism classification. We utilized data from a Brazilian courier company. In order to categorize absenteeism classes, we preprocessed the data, selected the attributes via multiple methods, balanced the dataset using the synthetic minority over-sampling method, and then employed four methods of machine learning classification: Support Vector Machine (SVM), Multinomial Logistic Regression (MLR), Artificial Neural Network (ANN), and Random Forest (RF). We selected the best model based on several validation scores, and compared its performance against the existing model. Furthermore, project managers may lack experience in machine learning, or may not have the time to spend developing machine learning algorithms. Thus, we propose a web-based interactive tool supported by cognitive analytics management (CAM) theory. The web-based decision tool enables managers to make more informed decisions, and can be used without any prior knowledge of machine learning. Understanding absenteeism patterns can assist managers in revising policies or creating new arrangements to reduce absences in the workplace, financial losses, and the probability of economic insolvency.

In epidemiological cohort studies, the relative risk (also known as risk ratio) is a major measure of association to summarize the results of two treatments or exposures. Generally, it measures the relative change in disease risk as a result of treatment application. Standard approaches to estimating relative risk available in common software packages may produce biased inference when applied to correlated binary data collected from longitudinal or clustered studies. In recent years, several methods for estimating the risk ratio for correlated binary data have been published, some of which maintain a well-controlled coverage probability but do not maintain an appropriate interval width or the interval location to measure the balance between distal and mesial noncoverage probabilities accurately or, vice versa. This paper develops efficient and straightforward inference procedures for estimating a confidence interval for risk ratio based on a hybrid method. In general, the hybrid method combines two separate confidence intervals for two single risk rates to form a hybrid confidence interval for their ratio. Additionally, we propose the procedures for constructing a confidence interval for risk ratio that directly extends recently recommended methods for correlated binary data by building on the concepts of the design effect and effective sample sizes typically used in representative sample surveys. In order to investigate the performance of these proposed methods, we conduct an extensive simulation study. To demonstrate the utility of our proposed methods, we present three examples from real-life applications, comparing the side effects of low-dose tricyclic antidepressants with a placebo, the efficacy of the treatment group in a teratological experiment, and the efficiency of the active drugs in curing infection for clinical trials.

Bacterial biofilms are prevalent in clinical environments, contributing to persistent infections associated with medical devices. Enterobacter cloacae forms biofilms on nonliving surfaces, leading to drug‐resistant, recurrent infections that are difficult to treat. Biofilm development in Enterobacter species, including E. cloacae , occurs through five stages: reversible attachment, irreversible attachment, microcolony formation, maturation and dispersal. Initial attachment is mediated by adhesins, including fimbriae and lipopolysaccharides, which interact with surfaces. This is followed by secretion of an extracellular polymeric substance matrix composed of polysaccharides, extracellular DNA and proteins, providing stability and protection. This study aimed to establish a standardised in vitro 0.5% crystal violet staining method to quantify biofilm production in E. cloacae isolates and classify isolates by biofilm‐forming capacity. Biofilm was quantified by optical density at 570–600 nm. A 96‐well microtiter plate assay quantified biofilm formation in 40 E. cloacae strains collected between July 2021 and April 2023. Growth conditions were optimised, including culture media, fixation techniques and additive concentrations of glucose and sodium chloride. Brain heart infusion broth was optimal, and heat fixation was superior; glucose had no effect, whereas 1%–2% sodium chloride enhanced biofilm production. These findings improve understanding of environmental regulation of biofilm formation and microbial persistence across habitats.