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Found 7 papers, 6 papers with code
KG-TREAT: Pre-training for Treatment Effect Estimation by Synergizing Patient Data with Knowledge Graphs
To address the challenges, we introduce a novel pre-training and fine-tuning framework, KG-TREAT, which synergizes large-scale observational patient data with biomedical knowledge graphs (KGs) to enhance TEE.
Heterogeneous treatment effect estimation with subpopulation identification for personalized medicine in opioid use disorder
Deep learning models have demonstrated promising results in estimating treatment effects (TEE).
SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification
Precise estimation of treatment effects is crucial for evaluating intervention effectiveness.
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning.
Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes
Although some treatment actions non-survivors received may be helpful, if confounders cause the mortality, the training of RL models guided by long-term outcomes (e. g., 90-day mortality) would punish those treatment actions causing the learned DTR policies to be suboptimal.
Estimating Individual Treatment Effects with Time-Varying Confounders
Estimating the individual treatment effect (ITE) from observational data is meaningful and practical in healthcare.
When deep learning meets causal inference: a computational framework for drug repurposing from real-world data
Drug repurposing is an effective strategy to identify new uses for existing drugs, providing the quickest possible transition from bench to bedside.
