Previously submitted to: JMIR Cancer (no longer under consideration since Mar 03, 2026)
Date Submitted: Nov 24, 2025
Open Peer Review Period: Nov 27, 2025 - Jan 22, 2026
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Transforming Breast Cancer Recurrence Prevention: AI-Powered Causal Discovery of Sociocultural Factors from Clinical Notes
ABSTRACT
Background:
Breast cancer recurrence (BCR) significantly impacts patient survival, quality of life, and overall treatment efficacy, underscoring the critical need to identify causal factors influencing recurrence. Although Sociocultural factors of Mental Health(SFOMHs) have been extensively associated with breast cancer outcomes, precise causal relationships remain poorly understood due to limitations in traditional correlational methods.
Objective:
The objective of this study is to (1) develop and evaluate a comprehensive, multi-step framework to rigorously detect and estimate the causal effects of 22 Sociocultural factors on BCR, and (2) benchmark the proposed framework against established causal models to ensure generalizability and reliability across diverse datasets.
Methods:
We first developed a Clinical Longformer Multi-Task Multi-Label Classifier (CLMT-MLC) to accurately detect and classify Sociocultural factors from unstructured clinical notes. Next, we designed a novel Siamese Neural Network based subgroup discovery (SNN-SD) method, combined with a Causal Effect Variational AutoEncoder (CEVAE), to estimate subgroup-specific Conditional Average Treatment Effects (CATE). A new dataset, SFOMH-OncoBreast-Clinic, comprising Sociocultural factor–annotated clinical notes and BCR annotations, was created in collaboration with experts and sub-sampled from the MIMIC-IV dataset. Performance was benchmarked against state-of-the-art causal models on the Infant Health and Development Program (IHDP) dataset.
Results:
The proposed framework significantly outperformed state-of-the-art causal models on the IHDP dataset. Applied to the SFOMH-OncoBreast-Clinic dataset, the model reliably identified actionable causal determinants of BCR, demonstrating its ability to advance understanding of Sociocultural factors as key predictors of recurrence.
Conclusions:
This study establishes a robust causal inference framework integrating Clinical Longformer, SNN-SD, and CEVAE, supported by a novel annotated dataset. The approach enhances detection of actionable Sociocultural factors, informing personalized care strategies and policy development to reduce breast cancer recurrence and health disparities.
Citation
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