Myocardial infarction is one of the most severe forms of ischemic heart disease, associated with high mortality and disability worldwide. The accurate and reliable prediction of adverse cardiovascular events is critical for developing effective treatment strategies and improving outcomes in cardiac rehabilitation. Traditional prognostic models, such as the GRACE and TIMI scores, often lack the flexibility to incorporate a wide range of contemporary clinical predictors. Therefore, machine learning methods, particularly deep neural networks, have recently emerged as promising alternatives capable of enhancing predictive accuracy and enabling more personalized care. This study presents a multitask deep learning model designed to simultaneously address two related tasks: multidimensional binary classification of myocardial infarction complications and multiclass classification of mortality causes. The model was trained on a dataset of 1700 patients, encompassing 111 clinical and demographic features. Experimental results demonstrate high predictive accuracy and the model’s capacity to capture complex interactions among risk factors, suggesting its potential as a valuable tool for clinical decision support in cardiology. Comparative analysis confirms that the proposed multitask approach performs comparably to, or better than, conventional machine learning models. Future research will focus on refining the model and validating its generalizability in real-world clinical environments.

This paper presents a comprehensive numerical modeling of nonlinear groundwater flow in a synthetic heterogeneous four-layer porous medium. Multilayered aquifer systems present significant modeling challenges due to nonlinear filtration and interlayer exchange processes. The mathematical model consists of four coupled nonlinear parabolic partial differential equations, where the nonlinearity arises from the dependence of hydraulic conductivity on hydraulic head. Vertical exchange between layers is described by Darcy’s law through separating aquicludes. The system is solved using a fully implicit finite-difference scheme by employing an alternating-direction implicit approach, resulting in a block-tridiagonal system of equations. The model is verified using analytical solutions and mass conservation tests. Application to a synthetic aquifer system demonstrates the model’s ability to reproduce complex transient behavior, including delayed response of upper layers to pumping and asymmetry of water-level drawdown cones due to nonlinear conductivity. The model’s greatest sensitivity is observed to the conductivity of the pumped layer and the vertical conductivity of the separating layers. The proposed approach represents a robust tool for groundwater management in structurally complex geological settings.

Physical clogging during managed aquifer recharge can reduce aquifer conductivity and alter leakage across intervening aquitards. This study presents a quasi-three-dimensional model coupling horizontal groundwater flow, suspended-particle transport, retention, porosity and conductivity change, and clogging-sensitive interlayer exchange in three heterogeneous aquifer units separated by two unresolved aquitards. Simulations start from the clean steady solution to isolate clogging effects from hydraulic redistribution. Flow is solved using an alternating-direction implicit scheme, while particle transport and retention use conservative advection–dispersion and positivity-preserving reaction steps. Verification is separated by level: the flow solver is second-order accurate in space against an exact solution (observed order 2.009), whereas the formally first-order coupled system, which has no analytical solution, is assessed through conservation, positivity, datum invariance, and grid/time refinement. After 200 days, mean conductivity in the recharge unit decreases by 1.038%. Relative to a matched clean-interface control, interface clogging changes mean interlayer leakage by −0.0528% and −0.0451%, a numerically resolved but physically very small effect. Sensitivity tests show that heterogeneity and constitutive assumptions affect response magnitude. These results are conditional on the synthetic parameter set and require calibration and independent physical validation before predictive use.