Enhancing Clinical Validation for Early Cardiovascular Disease Prediction through Simulation, AI, and Web Technology

Cardiovascular diseases (CVDs) remain a major global health challenge and a leading cause of mortality, highlighting the need for improved predictive models. We introduce an innovative agent-based dynamic simulation technique that enhances our AI models’ capacity to predict CVD progression. This method simulates individual patient responses to various cardiovascular risk factors, improving prediction accuracy and detail. Also, by incorporating an ensemble learning model and interface of web application in the context of CVD prediction, we developed an AI dashboard-based model to enhance the accuracy of disease prediction and provide a user-friendly app. The performance of traditional algorithms was notable, with Ensemble learning and XGBoost achieving accuracies of 91% and 95%, respectively. A significant aspect of our research was the integration of these models into a streamlit-based interface, enhancing user accessibility and experience. The streamlit application achieved a predictive accuracy of 97%, demonstrating the efficacy of combining advanced AI techniques with user-centered web applications in medical prediction scenarios. This 97% confidence level was evaluated by Brier score and calibration curve. The design of the streamlit application facilitates seamless interaction between complex ML models and end-users, including clinicians and patients, supporting its use in real-time clinical settings. While the study offers new insights into AI-driven CVD prediction, we acknowledge limitations such as the dataset size. In our research, we have successfully validated our predictive proposed methodology against an external clinical setting, demonstrating its robustness and accuracy in a real-world fixture. The validation process confirmed the model’s efficacy in the early detection of CVDs, reinforcing its potential for integration into clinical workflows to aid in proactive patient care and management. Future research directions include expanding the dataset, exploring additional algorithms, and conducting clinical trials to validate our findings. This research provides a valuable foundation for future studies, aiming to make significant strides against CVDs

Greedy kernel methods for center manifold approximation

For certain dynamical systems it is possible to significantly simplify the study of stability by means of the center manifold theory. This theory allows to isolate the complicated asymptotic behavior of the system close to a non-hyperbolic equilibrium point, and to obtain meaningful predictions of its behavior by analyzing a reduced dimensional problem. Since the manifold is usually not known, approximation methods are of great interest to obtain qualitative estimates. In this work, we use a data-based greedy kernel method to construct a suitable approximation of the manifold close to the equilibrium. The data are collected by repeated numerical simulation of the full system by means of a high-accuracy solver, which generates sets of discrete trajectories that are then used to construct a surrogate model of the manifold. The method is tested on different examples which show promising performance and good accuracy

Applying Sodium Profile with or without Ultrafiltration Profile Failed to Show Beneficial Effects on the Incidence of Intra- dialytic Hypotension in Susceptible Hemodilaysis Patients

Introduction: Intra-dialytic hypotension (IDH) is a common complication during hemodialysis (HD) treatment. Previous studies have reportedthat modulating dialysate sodium concentration combined or not with modulation of ultrafiltration (UF) rate may reduce the incidence of IDH. The aim of the present study was to evaluate the effect of sodium and UF profiles on the occurrence of intra-dialytic complications and dialysis quality.Methods: From a total of 64 patients, we selected 18 patients who suffered from recurrent IDH. Every patient received ten HD sessions utilizing each of the following treatments: (1) Control: constant sodium concentrationand UF rates. (2) Sodium and UF profiles: a linearly decreasing sodium concentration combined with a linearly decreasing UF rate. (3) Sodium profile:decreasing sodium concentration with constant UF rate.Results: Fourteen patients completed the study protocol. The incidence of IDH, mean inter-dialytic weight gain and the delivered dialysis dose were not different between the three treatments. However, symptomatic episodes of IDH were more commonand pre-dialysis systolic bloodpressure was higher during the second and third treatment modalities compared to controls. Isolated sodium profile was associated with more malaise and less achievement of target session duration compared to the other two treatments. Isolated sodium profile was associated withless achievement of target UF while combined sodium and UF profiles were associated with more achievement of target UF compared to controls.Conclusion: Our results indicate that sodium profile with or without UF profile does not have a beneficial effect on the incidence of IDH, achievement of target session duration or the delivered dialysis dose