Dep-LLM is a training-free three-stage LLM framework that decomposes clinical interviews into clinical themes, modulates signals by token entropy, and outperforms zero-shot and supervised baselines on DAIC-WOZ and E-DAIC datasets.
Large language models in biomedicine and healthcare,
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
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.
HyEm maps radius-controlled hyperbolic ontology embeddings to Euclidean space for ANN indexing and applies query-adaptive hyperbolic reranking to improve hierarchy-aware retrieval while preserving most Euclidean performance on flat queries.
citing papers explorer
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Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning
Dep-LLM is a training-free three-stage LLM framework that decomposes clinical interviews into clinical themes, modulates signals by token entropy, and outperforms zero-shot and supervised baselines on DAIC-WOZ and E-DAIC datasets.
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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.
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HyEm: Query-Adaptive Hyperbolic Retrieval for Biomedical Ontologies via Euclidean Vector Indexing
HyEm maps radius-controlled hyperbolic ontology embeddings to Euclidean space for ANN indexing and applies query-adaptive hyperbolic reranking to improve hierarchy-aware retrieval while preserving most Euclidean performance on flat queries.