THCM-CAL: Temporal-Hierarchical Causal Modelling with Conformal Calibration for Clinical Risk Prediction

TL;DR

THCM-CAL introduces a novel causal modeling framework that integrates structured and unstructured clinical data with conformal calibration, improving risk prediction accuracy and reliability in electronic health records.

Contribution

It presents a hierarchical causal discovery approach for multimodal clinical data and extends conformal prediction for multi-label ICD coding, enhancing predictive confidence.

Findings

Outperforms existing models on MIMIC datasets

Accurately infers causal interactions among clinical entities

Provides calibrated confidence intervals for risk predictions

Abstract

Automated clinical risk prediction from electronic health records (EHRs) demands modeling both structured diagnostic codes and unstructured narrative notes. However, most prior approaches either handle these modalities separately or rely on simplistic fusion strategies that ignore the directional, hierarchical causal interactions by which narrative observations precipitate diagnoses and propagate risk across admissions. In this paper, we propose THCM-CAL, a Temporal-Hierarchical Causal Model with Conformal Calibration. Our framework constructs a multimodal causal graph where nodes represent clinical entities from two modalities: Textual propositions extracted from notes and ICD codes mapped to textual descriptions. Through hierarchical causal discovery, THCM-CAL infers three clinically grounded interactions: intra-slice same-modality sequencing, intra-slice cross-modality triggers, and inter-slice risk propagation. To enhance prediction reliability, we extend conformal prediction to multi-label ICD coding, calibrating per-code confidence intervals under complex co-occurrences. Experimental results on MIMIC-III and MIMIC-IV demonstrate the superiority of THCM-CAL.