PEQ-Net jointly estimates multiple longitudinal treatment policies via a shared policy encoder and kernel mean embeddings to constrain second-order bias after LTMLE correction.
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2026 2verdicts
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RePrompT uses recurrent prompt tuning to inject prior-visit latent states and cohort-derived population prompt tokens into LLMs, yielding better performance than pure EHR or pure LLM baselines on MIMIC clinical prediction tasks.
citing papers explorer
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Smooth Multi-Policy Causal Effect Estimation in Longitudinal Settings
PEQ-Net jointly estimates multiple longitudinal treatment policies via a shared policy encoder and kernel mean embeddings to constrain second-order bias after LTMLE correction.
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RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models
RePrompT uses recurrent prompt tuning to inject prior-visit latent states and cohort-derived population prompt tokens into LLMs, yielding better performance than pure EHR or pure LLM baselines on MIMIC clinical prediction tasks.