Identification of the Framingham Risk Score by an Entropy-Based Rule Model for Cardiovascular Disease

Estimated pulse wave velocity is associated with all-cause and cardiovascular mortality in individuals with stroke: A national-based prospective cohort study

Extensive evidence underscores the potential of estimated pulse wave velocity (ePWV) as a robust tool for predicting disease prevalence and mortality. However, its comparative effectiveness in forecasting all-cause and cardiovascular disease (CVD) mortality, particularly among stroke populations, remains inadequately characterized in relation to the traditional Framingham Risk Score (FRS) model. This prospective study included 1202 individuals with stroke from the National Health and Nutrition Examination Survey conducted between 1999 and 2014, with comprehensive follow-up data. Survey-weighted Cox regression models were employed to examine the association between ePWV and the risks of all-cause and CVD mortality. Subgroup analyses were performed to evaluate the stability of ePWV in predicting these outcomes. A generalized additive model was utilized to explore the dose-response relationship between ePWV and mortality risk. Receiver operating characteristic curves were then used to assess and compare the prognostic capabilities of ePWV and FRS models for 10-year all-cause and CVD mortality. After adjustment for relevant covariates, each 1 m/s increase in ePWV was associated with a 44% and 65% increase in all-cause and CVD mortality, respectively. ePWV demonstrated consistent prognostic performance across the majority of stroke subpopulations. Notably, ePWV exhibited a nonlinear relationship with all-cause mortality (P for nonlinearity = .045) while maintaining a linear association with CVD mortality (P for nonlinearity = .293). Furthermore, ePWV outperformed the FRS model in predicting 10-year all-cause (Integrated Discrimination Improvement = 0.061, 95% confidence interval: 0.031-0.095, P = .007) and CVD mortality (95% confidence interval: 0.005-0.083, P = .02). ePWV is an independent risk factor for both all-cause and CVD mortality in individuals with stroke, demonstrating superior predictive value compared to the traditional FRS model for forecasting these outcomes.

Machine Learning Models for Cardiovascular Disease Events Prediction

Cardiovascular diseases (CVDs) are among the most serious disorders leading to high mortality rates worldwide. CVDs can be diagnosed and prevented early by identifying risk biomarkers using statistical and machine learning (ML) models, In this work, we utilize clinical CVD risk factors and biochemical data using machine learning models such as Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Extreme Grading Boosting (XGB) and Adaptive Boosting (AdaBoost) to predict death caused by CVD within ten years of follow-up. We used the cohort of the Ludwigshafen Risk and Cardiovascular Health (LURIC) study and 2943 patients were included in the analysis (484 annotated as dead due to CVD). We calculated the Accuracy (ACC), Precision, Recall, F1-Score, Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. The findings of the comparative analysis show that Logistic Regression has been proven to be the most reliable algorithm having accuracy 72.20 %. These results will be used in the TIMELY study to estimate the risk score and mortality of CVD in patients with 10-year risk.

Cognitive Based Authentication Protocol for Distributed Data and Web Technologies

The objective of the verification process, besides guaranteeing security, is also to be effective and robust. This means that the login should take as little time as possible, and each time allow for a successful authentication of the authorised account. In recent years, however, online users have been experiencing more and more issues with recalling their own passwords on the spot. According to research done in 2017 by LastPass on its employees, the number of personal accounts assigned to one business user currently exceeds 191 profiles and keeps growing. Remembering these many passwords, especially to applications which are not used every week, seems to be impossible without storing them either on paper, in a password manager, or saved in a file somewhere on a PC. In this article a new verification model using a Google Street View image as well as the user’s personal experience and knowledge will be presented. The purpose of this scheme is to assure secure verification by creating longer passwords as well as delivering a ‘password reminder’ already embedded into the login scheme.

Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?

Analysis of pulmonary function tests (PFTs) is an area where machine learning (ML) may benefit clinicians, researchers, and the patients. PFT measures spirometry, lung volumes, and carbon monoxide diffusion capacity of the lung (DLCO). The results are usually interpreted by the clinicians using discrete numeric data according to published guidelines. PFT interpretations by clinicians, however, are known to have inter-rater variability and the inaccuracy can impact patient care. This variability may be caused by unfamiliarity of the guidelines, lack of training, inadequate understanding of lung physiology, or simply mental lapses. A rules-based automated interpretation system can recapitulate expert’s pattern recognition capability and decrease errors. ML can also be used to analyze continuous data or the graphics, including the flow-volume loop, the DLCO and the nitrogen washout curves. These analyses can discover novel physiological biomarkers. In the era of wearables and telehealth, particularly with the COVID-19 pandemic restricting PFTs to be done in the clinical laboratories, ML can also be used to combine mobile spirometry results with an individual’s clinical profile to deliver precision medicine. There are, however, hurdles in the development and commercialization of the ML-assisted PFT interpretation programs, including the need for high quality representative data, the existence of different formats for data acquisition and sharing in PFT software by different vendors, and the need for collaboration amongst clinicians, biomedical engineers, and information technologists. Hurdles notwithstanding, the new developments would represent significant advances that could be the future of PFT, the oldest test still in use in clinical medicine.