Model-Based Glycaemic Control in Multicentre ICUs within Diabetic Patients: In-silico Analysis

Sliding-scale insulin therapy has been vastly used for glycaemic control but dysglycaemia remains high. Model-based glycaemic control that incorporates insulin nutrition protocol was proposed as this therapy provides personalized care to avoid dysglycaemia. Thus, this paper aims to implement in-silico simulation and identify which model-based control protocols yield better protocol within ICU diabetic patients based on performance and safety. Multicentre ICU patients of 282 were divided into diabetes mellitus (DM) and non-diabetes mellitus (NDM) cohort where in-silico simulations were done using Specialised Relative Insulin Nutrition Therapy (SPRINT), SPRINT+Glargine and Stochastic Targeted (STAR) protocols. Performance was verified based on the percentage of blood glucose (BG) time in band (TIB) 6.0 – 10.0 mmol/L and safety with number of mild and severe hypoglycaemia episodes. Among the three protocols, STAR protocol showed the highest median and interquartile range % BG TIB 6.0 – 10.0 mmol/L for DM and NDM patients with 71.6 % [57.9 – 79.8] and 77.4 % [62.9 – 88.8]. The number of hypoglycaemia episodes are the lowest in DM and NDM patients too compared to other protocols. These advantages show that STAR protocol can provide better patient outcomes for glycaemic control with personalized care

Machine Learning Classifications of Multiple Organ Failures in a Malaysian Intensive Care Unit

Multiple organ failures are the main cause of mortality and morbidity in the intensive care unit (ICU). The progression of organ failures in the ICU is usually monitored using the Sequential Organ Failure Assessment (SOFA) score. This study aims to perform the classification of multiple organ failures using machine learning algorithms based on SOFA score. Ninety-eight ICU patients’ data were obtained retrospectively from Universiti Malaya Medical Centre for analysis. Several machine learning algorithms which are decision tree, linear discriminant, naïve Bayes, support vector machines, k-nearest neighbor, AdaBoost, and random forest were used for the classification. The classifiers were trained on 80% of the patients with 10-fold cross-validations and assessed on 20% of patients using 34 variables in the ICU. The random forest algorithm was able to achieve 99.8% accuracy and 99.9% sensitivity in the training dataset. Meanwhile, the AdaBoost algorithm achieved 99.1% sensitivity in the testing dataset. This study demonstrates the performances of different machine learning algorithms in the classification of multiple organ failures. The feature selection shows respiratory rate and mean arterial pressure (MAP) as the most important variables using chi-square test while insulin and fraction of oxygenated hemoglobin are the most important predictors by the mutual information test

Enhancing public health strategies for tungiasis: A mathematical approach with fractional derivative

In this study, we formulate a mathematical model in the framework of the Atangana-Baleanu fractional derivative in Caputo sense to study the transmission of tungiasis. In this formulation, interactions between the human host and the sand fleas are taken into consideration, including factors like infestation rate, incubation duration, and recovery rate. We calculate the basic reproduction parameter for the system, symbolized by $\mathcal{R}_0$ with the help of the next-generation matrix technique. A novel numerical scheme for encapsulating the non-local and memory-dependent aspects of the system is conceptualized via the Atangana-Baleanu fractional derivative. We prove the existence and uniqueness of the solution of the model of the infection and establish stability of the steady-states of the model. In addition to this, numerical simulations are carried out to evaluate the efficiency of interventions like campaigns for better sanitation and treatment, and to investigate the influence of various management techniques on the prevalence of tungiasis. The outcomes of the numerical simulations give us information about the possible efficacy of different control strategies in lowering the incidence of tungiasis. This research gives quantitative tools to enhance decision-making processes in public health treatments and advances our understanding of the dynamics of the tungiasis