Comprehensive Survival Prediction by Integrating Multi-grained Multi-modal Interactions
The core message of this article is that by leveraging the inherent hierarchical structure of whole slide images (WSIs) and transcriptomic data, and efficiently modeling intra-modal and inter-modal interactions at different granularity levels, the proposed SurvMamba framework can achieve superior performance in survival prediction compared to existing state-of-the-art methods, while also being computationally more efficient.
Efficient Binary Child Drawing Development Optimization for Feature Selection and Classification of Medical Datasets
A novel binary version of the Child Drawing Development Optimization (BCDDO) algorithm is proposed for efficient feature selection to improve classification accuracy on various medical datasets.
Federated Learning Model for Predicting Major Postoperative Complications
Federated learning models can accurately predict major postoperative complications using electronic health record data from multiple institutions, while preserving data privacy.
Bayesian Deep Learning for Survival Analysis: Quantifying Uncertainty in Prognostic Outcomes
Bayesian neural network architectures can provide uncertainty estimates in survival analysis, improving prediction performance and calibration compared to traditional non-Bayesian approaches.
Flexible Deep Learning Framework for Joint Modeling of Longitudinal Data, Recurrent Events, and Survival Outcomes
TransformerLSR is a novel deep learning framework that can jointly model longitudinal measurements, recurrent events, and survival outcomes, while accounting for their complex dependencies and incorporating known clinical knowledge.
Transformer-based Diffusion Probabilistic Model for Accurate and Efficient Forecasting of Heart Rate and Blood Pressure in Intensive Care Units
A novel deep learning approach, Transformer-based Diffusion Probabilistic Model for Sparse Time Series Forecasting (TDSTF), achieves state-of-the-art performance in predicting heart rate, systolic blood pressure, and diastolic blood pressure in the intensive care unit setting.
Bayesian Neural Controlled Differential Equations for Uncertainty-Aware Treatment Effect Estimation in Continuous Time
Our Bayesian neural controlled differential equation (BNCDE) model provides meaningful posterior predictive distributions of potential outcomes, enabling reliable decision-making in personalized medicine.
Multilevel Stochastic Optimization for Imputing Missing Values in Massive Medical Data Records
A multilevel stochastic optimization approach based on computational applied mathematics techniques can accurately and efficiently impute missing values in massive medical datasets, significantly outperforming current state-of-the-art methods.
Predicting Mortality, ICU Admission, and Ventilation Support Requirement for COVID-19 Patients Using 122 Parameters and Artificial Intelligence
Appropriate machine learning algorithms with carefully selected features and balanced data can accurately predict mortality, ICU requirement, and ventilation support for COVID-19 patients.
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