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 |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194018803&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194018803 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2026 vid: 336 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 194018803 194018803 194018803 10.3233/SHTI260165 194018803 ppf: 298 ppct: 5 formats: tig: atl: 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. aug: au: AZZIMONTI, Laura LENATTI, Marta ZAFFALON, Marco SIMEONE, Davide GUERGACHI, Aziz KESHAVJEE, Karim MONGELLI, Maurizio PAGLIALONGA, Alessia affil: IDSIA USI-SUPSI, SUPSI, 6900, Lugano, Switzerland. sug: subj: Primary Health Care Canada Diabetes Mellitus, Type 2 Risk Factors Risk Assessment Causality Expert Clinicians Data Management Electronic Health Records Human Retrospective Design Record Review Congresses and Conferences Italy Italy Canada Knowledge Diabetes Mellitus, Type 2 Symptoms Prediction Models Decision Support Systems, Clinical Diabetes Mellitus, Type 2 Prevention and Control Nonexperimental Studies Descriptive Statistics Data Analysis Software Cholesterol Body Mass Index Blood Glucose ROC Curve Confidence Intervals Odds Ratio Funding Source ab: 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. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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