THCM - CAL : Temporal-Hierarchical Causal Modelling with Conformal Calibration for Clinical Risk Prediction (2025)

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**.

## introduction accurate clinical risk prediction from electronic health records (ehrs) @xcite is essential for enabling timely clinical interventions and improving treatment effects @xcite . ehrs comprise two complementary data modalities: structured diagnostic codes drawn from the international classification of diseases (icd) @xcite and unstructured narrative notes that chronicle patient observations and interventions over time @xcite . leveraging both modalities can enhance automated code assignment @xcite , risk stratification @xcite , and downstream decision support @xcite . previous approaches as shown in ## related work we categorize prior work into three areas: textcentric diagnosis prediction, temporal causal discovery, and uncertainty quantification. ## experiments to rigorously evaluate thcm-cal, we conduct comprehensive experiments across several baseline clinical language models and benchmark its diagnostic-prediction performance against state-ofthe-art methods. ## conlcusion in this work, we introduced thcm-cal, a unified temporal-hierarchical causal model with conformal calibration for clinical risk prediction. by constructing a multi-slice causal graph that jointly captures intra-visit proposition sequencing, intra-visit cross-modality triggers, and inter-visit risk propagation, our model uncovers clinically meaningful relationships between narrative observations and diagnostic codes. we further adapt split conformal prediction to the multi-label setting, providing finite-sample guarantees on per-code. extensive experiments on mimic-iii and mimic-iv benchmarks demonstrate that thcm-cal substantially outperforms state-of-the-art baselines, while offering calibrated uncertainty estimates. ablation studies confirm that each causal edge type and the conformal calibration module are critical to performance gains.