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Found 8 papers, 3 papers with code
Towards AI-Based Precision Oncology: A Machine Learning Framework for Personalized Counterfactual Treatment Suggestions based on Multi-Omics Data
AI-driven precision oncology has the transformative potential to reshape cancer treatment by leveraging the power of AI models to analyze the interaction between complex patient characteristics and their corresponding treatment outcomes.
Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes
In this paper, we propose a deep generative time series approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories.
Dynamic Local Attention with Hierarchical Patching for Irregular Clinical Time Series
Irregular multivariate time series data is prevalent in the clinical and healthcare domains.
Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models
We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies.
SimTS: Rethinking Contrastive Representation Learning for Time Series Forecasting
Contrastive learning methods have shown an impressive ability to learn meaningful representations for image or time series classification.
Correlated Product of Experts for Sparse Gaussian Process Regression
Gaussian processes (GPs) are an important tool in machine learning and statistics with applications ranging from social and natural science through engineering.
Orthogonally Decoupled Variational Fourier Features
Sparse inducing points have long been a standard method to fit Gaussian processes to big data.
Recursive Estimation for Sparse Gaussian Process Regression
Gaussian Processes (GPs) are powerful kernelized methods for non-parameteric regression used in many applications.
