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Heart disease is a serious worldwide health issue with wide-reaching effects. Since heart disease is one of the leading causes of mortality worldwide, early detection is crucial. Emerging technologies like Machine Learning (ML) are currently being actively used by the biomedical, healthcare, and health prediction industries. PaRSEL, a new stacking model is proposed in this research, that combines four classifiers, Passive Aggressive Classifier (PAC), Ridge Classifier (RC), Stochastic Gradient Descent Classifier (SGDC), and eXtreme Gradient Boosting (XGBoost), at the base layer, and LogitBoost is deployed for the final predictions at the meta layer. The imbalanced and irrelevant features in the data increase the complexity of the classification models. The dimensionality reduction and data balancing approaches are considered very important for lowering costs and increasing the accuracy of the model. In PaRSEL, three dimensionality reduction techniques, Recursive Feature Elimination (RFE), Linear Discriminant Analysis (LDA), and Factor Analysis (FA), are used to reduce the dimensionality and select the most relevant features for the diagnosis of heart disease. Furthermore, eight balancing techniques, Proximity Weighted Random Affine Shadowsampling (ProWRAS), Localized Randomized Affine Shadowsampling (LoRAS), Random Over Sampling (ROS), Adaptive Synthetic (ADASYN), Synthetic Minority Oversampling Technique (SMOTE), Borderline SMOTE (B-SMOTE), Majority Weighted Minority Oversampling Technique (MWMOTE) and Random Walk Oversampling (RWOS), are used to deal with the imbalanced nature of the dataset. The performance of PaRSEL is compared with the other standalone classifiers using different performance measures like accuracy, F1-score, precision, recall and AUC-ROC score. Our proposed model achieves 97% accuracy, 80% F1-score, precision is greater than 90%, 67% recall, and 98% AUC-ROC score. This shows that PaRSEL outperforms other standalone classifiers in terms of heart disease prediction. Additionally, we deploy SHapley Additive exPlanations (SHAP) on our proposed model. It helps to understand the internal working of the model. It illustrates how much influence a classifier has on the final prediction outcome.

Efficient data dissemination is a key challenge for IoT applications. In this paper, we present an ergodic achievable rate analysis of the non-orthogonal multiple access (NOMA)-based cooperative transmission scheme in spectrum-sharing cognitive radio networks (CRNs). The transmission scheme consists of two phases, where the NOMA transmission strategy is employed at the secondary transmitter (ST) in the second phase to serve the primary receiver (PR) and secondary receivers (SRs). This cooperative spectrum-sharing transmission is favourable when the ST-PR link is strong and the ST faces a spectrum scarcity issue. To evaluate the performance of the network over Nakagami-m channels, ergodic achievable rates at PR and SRs are derived in closed forms, which can be used for any integer or non-integer value of fading index m. Finally, we compare the results of the analytical expressions with simulated results for validation.

Type-1 diabetes mellitus (T1DM) is a challenging disorder which essentially involves regulation of the glucose levels to avoid hyperglycemia as well as hypoglycemia. For this purpose, this research paper proposes and develops control algorithms using an intelligent predictive control model, which is based on a UVA/Padova metabolic simulator. The primary objective of the designed control laws is to provide an automatic blood glucose control in insulin-dependent patients so as to improve their life quality and to reduce the need of an extremely demanding self-management plan. Various linear and nonlinear control algorithms have been explored and implemented on the estimated model. Linear techniques include the Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR), and nonlinear control strategy includes the Sliding Mode Control (SMC), which are implemented in this research work for continuous monitoring of glucose levels. Performance comparison based on simulation results demonstrated that SMC proved to be most efficient in terms of regulating glucose profile to a reference level of 70 mg/dL compared to the classical linear techniques. A brief comparison is presented between the linear techniques (PID and LQR), and nonlinear technique (SMC) for analysis purposes proving the efficacy of the design.

Continuous adjustment of Propofol in manual delivery of anesthesia for conducting a surgical procedure overburdens the workload of an anesthetist who is working in a multi-tasking scenario. Going beyond manual administration and Target Controlled Infusion, closed-loop control of Propofol infusion has the potential to offer several benefits in terms of handling perturbations and reducing the effect of inter-patient variability. This paper proposes a closed-loop automated drug administration approach to control Depth Of Hypnosis in anesthesia. In contrast with most of the existing research on anesthesia control which makes use of linear control strategies or their improved variants, the novelty of the present research lies in applying robust control strategy i.e. Sliding Mode Control to accurately control drug infusion. Based on the derived patient's model, the designed controller uses measurements from EEG to regulate DOH on Bispectral Index by controlling infusion rate of Propofol. The performance of the controller is investigated and characterized with real dataset of 8 patients undergoing surgery. Results of this in silico study indicate that for all the patients, with 0% overshoot observed, the steady state error lies in between ±5. Clinically, this implies that in all the cases, without any overdose, the controller maintains the desired DOH level for smooth conduction of surgical procedures. Resumo: O ajuste contínuo de propofol na administração manual de anestesia para a realização de um procedimento cirúrgico onera a carga de trabalho de anestesiologistas que trabalham em ambiente multitarefa. Indo além da administração manual e da infusão alvo-controlada (IAC), o controle de circuito fechado da infusão de propofol tem o potencial de oferecer vários benefícios em termos de manejo das perturbações e reduzir o efeito da variabilidade interpaciente. Este artigo propõe uma abordagem para a administração automatizada de drogas em circuito fechado para controlar a profundidade da hipnose (PDH) em anestesia. Em contraste com a maioria das pesquisas existentes sobre o controle da anestesia que usam estratégias de controle linear ou de suas variantes melhoradas, a novidade da presente pesquisa reside na aplicação de uma estratégia de controle robusto; isto é, o Controle por Modos Deslizantes (CMD) para controlar com precisão a infusão da droga. Com base no modelo derivado do paciente, o controlador projetado usa as medições do EEG para regular a PDH no Bispectral Index (BIS), controlando a taxa de infusão de propofol. O desempenho do controlador é investigado e caracterizado com um conjunto de dados reais de oito pacientes submetidos à cirurgia. Os resultados deste estudo in silico indicam que, para todos os pacientes, com 0% de excesso observado, o erro de estado estacionário fica entre ± 5. Clinicamente, isso implica que em todos os casos, sem qualquer sobredosagem, o controlador mantém o nível desejado de PDH para a condução tranquila dos procedimentos cirúrgicos. Keywords: Closed-loop anesthesia, Modern control, Biocontrol, Pharmacodynamics, Pharmacokinetics, Palavras-chave: Anestesia de circuito fechado, Controle moderno, Biocontrole, Farmacodinâmica, Farmacocinética

ABSTRACT Background Hepatitis B virus (HBV) infection causes liver disease, including hepatocellular carcinoma. Controlling viral activity is crucial to reducing complications. Tenofovir may offer benefits over entecavir, but it is unclear if switching from entecavir to tenofovir improves outcomes. This study assesses the clinical impact of switching to tenofovir therapy for chronic HBV infection. Methods Following the PRISMA guidelines, we conducted a literature search within the Cochrane Library, PubMed, MEDLINE, Embase, and Scopus for studies of patients with HBV infection who were switched to tenofovir from entecavir or were maintained on entecavir. Both formulations of tenofovir, that is, tenofovir disoproxil fumarate and tenofovir alafenamide were included and analyzed in subgroup analysis. Meta‐analyses were performed with RevMan 5.4 using a random‐effects model, with statistical significance set at p

ABSTRACT Introduction Gastroesophageal reflux disease (GERD) affects approximately 20% of adults in the United States. Proton pump inhibitors are the first‐line treatment but are associated with long‐term side effects. Endoscopic full‐thickness plication (EFTP) is a minimally invasive alternative that improves the valvular mechanism of the gastroesophageal junction. This meta‐analysis compared EFTP to a sham procedure for the treatment of refractory GERD. Materials and Methods This meta‐analysis followed the Cochrane guidelines and PRISMA standards and was registered with PROSPERO (CRD42023485506). We searched MEDLINE, Embase, SCOPUS, and Cochrane Library through December 2023. Inclusion criteria targeted Randomized controlled trials comparing EFTP with sham procedures for GERD were included. Statistical analyses utilized RevMan with a random‐effects model, and the results were considered significant at p

Abstract Continuous adjustment of Propofol in manual delivery of anesthesia for conducting a surgical procedure overburdens the workload of an anesthetist who is working in a multi-tasking scenario. Going beyond manual administration and Target Controlled Infusion, closed-loop control of Propofol infusion has the potential to offer several benefits in terms of handling perturbations and reducing the effect of inter-patient variability. This paper proposes a closed-loop automated drug administration approach to control Depth Of Hypnosis in anesthesia. In contrast with most of the existing research on anesthesia control which makes use of linear control strategies or their improved variants, the novelty of the present research lies in applying robust control strategy i.e. Sliding Mode Control to accurately control drug infusion. Based on the derived patient's model, the designed controller uses measurements from EEG to regulate DOH on Bispectral Index by controlling infusion rate of Propofol. The performance of the controller is investigated and characterized with real dataset of 8 patients undergoing surgery. Results of this in silico study indicate that for all the patients, with 0% overshoot observed, the steady state error lies in between ±5. Clinically, this implies that in all the cases, without any overdose, the controller maintains the desired DOH level for smooth conduction of surgical procedures.