Papers with prediction reliability

3 papers
THCM-CAL: Temporal-Hierarchical Causal Modelling with Conformal Calibration for Clinical Risk Prediction (2025.findings-emnlp)

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Challenge: Existing approaches to risk prediction from EHRs handle structured diagnostic codes and unstructured narrative notes separately.
Approach: They propose a Temporal-Hierarchical Causal Model with Conformal Calibration . they construct a multimodal causal graph where nodes represent clinical entities from two modalities .
Outcome: The proposed model infers three clinically grounded interactions from textual propositions and ICD codes mapped to textual descriptions.
Exploring Explanations Improves the Robustness of In-Context Learning (2025.acl-long)

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Challenge: In-context learning (ICL) has been shown to be effective across a variety of tasks, but it has been reported to be restricted in its ability to generalize beyond the given demonstrations.
Approach: They propose a framework that extends ICL by exploring explanations for all possible labels.
Outcome: The proposed framework improves prediction reliability by exploring explanations for all possible labels.
HCRE: LLM-based Hierarchical Classification for Cross-Document Relation Extraction with a Prediction-then-Verification Strategy (2026.findings-acl)

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Challenge: Existing approaches to cross-document relation extraction (RE) focus on identifying relations between head and tail entities from single sentence or document.
Approach: They propose a hierarchical relation tree-based LLM-based hierarchic classification model for cross-document relation extraction (HCRE) based on predefined relations, the model can perform hierarchically classification level by level.
Outcome: The proposed model outperforms existing baselines and validates its effectiveness.

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