OntoBioStat: Supporting Causal Diagram Design and Analysis

Autor: Thibaut Pressat Laffouilhère, Julien Grosjean, Jacques Bénichou, Stefan J. Darmoni, Lina F. Soualmia
Přispěvatelé: Département d'Informatique Médicale (D2IM), CHU Rouen, Normandie Université (NU)-Normandie Université (NU), Unité de biostatistiques [CHU Rouen], Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU), Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA), Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé (LIMICS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Sorbonne Paris Nord, Mode de vie, génétique et santé : études intégratives et transgénérationnelles (U1018 (Équipe 9)), Institut Gustave Roussy (IGR)-Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, LEJEUNE, Emeline
Rok vydání: 2022
Předmět:
Zdroj: Studies in Health Technology and Informatics
Studies in Health Technology and Informatics, 2022, Studies in Health Technology and Informatics, 294, pp.302-306. ⟨10.3233/SHTI220463⟩
ISSN: 1879-8365
0926-9630
DOI: 10.3233/SHTI220463⟩
Popis: International audience; Suitable causal inference in biostatistics can be best achieved by knowledge representation thanks to causal diagrams or directed acyclic graphs. However, necessary and sufficient causes are not easily represented. Since existing ontologies do not fill this gap, we designed OntoBioStat in order to enable covariate selection support based on causal relation representations. OntoBioStat automatic ontological causal diagram construction and inferences are detailed in this study. OntoBioStat inferences are allowed by Semantic Web Rule Language rules and axioms. First, statements made by the users include outcome, exposure, covariate, and causal relation specification. Then, reasoning enable automatic construction using generic instances of Meta_Variable and Necessary_Variable classes. Finally, inferred classes highlighted potential bias such as confounder-like. Ontological causal diagram built with OntoBioStat was compared to a standard causal diagram (without OntoBioStat) in a theoretical study. It was found that confounding and bias were not completely identified by the standard causal diagram, and erroneous covariate sets were provided. Further research is needed in order to make OntoBioStat more usable.
Databáze: OpenAIRE
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