PUICL is a transformer pretrained on synthetic PU data from structural causal models that solves positive-unlabeled classification via in-context learning without gradient updates or fitting.
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FeatEHR-LLM uses LLMs with tool-augmented code generation on dataset schemas to extract clinically meaningful features from irregular EHR time series, achieving the highest AUROC on 7 of 8 ICU prediction tasks with gains up to 6 points over baselines.
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In-Context Positive-Unlabeled Learning
PUICL is a transformer pretrained on synthetic PU data from structural causal models that solves positive-unlabeled classification via in-context learning without gradient updates or fitting.
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FeatEHR-LLM: Leveraging Large Language Models for Feature Engineering in Electronic Health Records
FeatEHR-LLM uses LLMs with tool-augmented code generation on dataset schemas to extract clinically meaningful features from irregular EHR time series, achieving the highest AUROC on 7 of 8 ICU prediction tasks with gains up to 6 points over baselines.