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Published on: July 5, 2022
Data-Driven and Expert-Informed Causal Discovery for Type 2 Diabetes Risk in Primary Care
Laura Azzimonti1, Marta Lenatti2, Marco Zaffalon1
1IDSIA USI-SUPSI, SUPSI, 6900, Lugano, Switzerland.
None:
The aim of this study is to extract causal relationships from a large set of routinely collected primary care data (biomarkers, medical conditions, risk factors, and medications) from across Canada, with a focus on Type 2 Diabetes risk. The causal discovery process combined data-driven insights with prior expert knowledge, using iterative refinement to construct a causal Directed Acyclic Graph (DAG). The retrieved DAG, which aligns with medical knowledge and performs satisfactorily in predicting future Type 2 Diabetes onset, could serve as a foundation for developing interpretable tools to support medical decision-making.
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Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...
