Data-Driven and Expert-Informed Causal Discovery for Type 2 Diabetes Risk in Primary Care...36th Medical Informatics Europe (MIE) Conference, May 25-28, 2026, Genoa, Italy.
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...
| Publicado en: | Studies in Health Technology & Informatics Vol. 336; pp. 298 - 303 |
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| Autores principales: | , , , , , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | 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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