arXiv:2606.24488v1 Announce Type: cross Abstract: Learning causal models from fragmented biomedical data is challenging because clinical, molecular, and imaging variables are often incomplete or not jointly observed. We propose RetiSEM, a domain-constrained structural equation modelling (SEM) framework for causal graph recovery and mediation analysis under limited multimodal resources. This proposed work organises variables into biologically informed blocks, applies forbidden-edge constraints, and decomposes pathway-level effects into TE, NDE, and NIE components. We evaluate RetiSEM across ten
The proliferation of fragmented, multimodal biomedical datasets necessitates new computational approaches to extract causal insights as AI continues to mature.
This work represents progress in applying sophisticated AI techniques to complex biomedical data, potentially accelerating drug discovery, personalized medicine, and our understanding of disease.
The ability to accurately model causality and perform mediation analysis from incomplete biomedical data becomes more feasible, enhancing the utility of existing and future datasets.
- · Biomedical researchers
- · Pharmaceutical companies
- · AI/ML developers in healthcare
- · Personalized medicine initiatives
- · Traditional statistical modeling approaches in fragmented data scenarios
- · Diseases with complex, multi-factorial causes
Improved understanding of disease pathways and more targeted therapeutic interventions.
Reduced timelines and costs for drug development, leading to a faster transition of research into clinical practice.
The integration of such models directly into clinical decision-making systems, enabling more proactive and precise patient care.
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Read at arXiv cs.AI