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

Abstract

THCM-CAL (Temporal-Hierarchical Causal Modelling with Conformal Calibration) is a research framework designed for automated clinical risk prediction using electronic health records (EHRs). It addresses the challenge of predicting clinical risks by integrating two distinct data modalities found in EHRs: structured diagnostic codes and unstructured narrative notes. The framework constructs a causal graph where nodes represent clinical entities from both modalities. Unlike previous methods that treat these modalities separately or use simple fusion, THCM-CAL models directional, hierarchical causal interactions, inferring three types of clinically grounded interactions: intra-slice same-modality sequencing, intra-slice cross-modality triggers, and inter-slice risk propagation. To improve the reliability of predictions, the model incorporates conformal prediction for multi-label ICD coding, calibrating confidence intervals for individual codes. Experimental results on MIMIC-III and MIMIC-IV datasets demonstrate the effectiveness of THCM-CAL.

Qiyu Wei 魏启宇

Ph.D student.

My research interests include AI in Semiconductor, LLM agents and Bayesian Optimisation.