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Found 10 papers, 1 papers with code
Estimating heterogeneous treatment effect from survival outcomes via (orthogonal) censoring unbiased learning
In this work, we develop censoring unbiased transformations (CUTs) for survival outcomes both with and without competing risks. After converting time-to-event outcomes using these CUTs, direct application of HTE learners for continuous outcomes yields consistent estimates of heterogeneous cumulative incidence effects, total effects, and separable direct effects.
Latent Space Explorer: Visual Analytics for Multimodal Latent Space Exploration
Machine learning models built on training data with multiple modalities can reveal new insights that are not accessible through unimodal datasets.
Efficient estimation of weighted cumulative treatment effects by double/debiased machine learning
Estimators targeting overlap weighted effects have been proposed to address the challenge of poor overlap, and methods enabling flexible machine learning for nuisance models address model misspecification.
Post-hoc loss-calibration for Bayesian neural networks
Bayesian decision theory provides an elegant framework for acting optimally under uncertainty when tractable posterior distributions are available.
Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains.
Modeling Disease Progression Trajectories from Longitudinal Observational Data
We learn disease progression patterns using Hidden Markov Models (HMM) and distill them into distinct trajectories using visualization methods.
Dynamic Knowledge Distillation for Black-box Hypothesis Transfer Learning
In real world applications like healthcare, it is usually difficult to build a machine learning prediction model that works universally well across different institutions.
DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways
Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records.
Unsupervised learning with contrastive latent variable models
Here, we present a probabilistic model for dimensionality reduction to discover signal that is enriched in the target dataset relative to the background dataset.
Simultaneous Modeling of Multiple Complications for Risk Profiling in Diabetes Care
The proposed method is favorable for healthcare applications because in additional to improved prediction performance, relationships among the different risks and risk factors are also identified.
