Patient and Graph Embeddings for Predictive Diagnosis of Drug Iatrogenesis

Lina F. Soualmia (1, 2) , Vincent Lafon (3) , Stéfan Darmoni (4, 2)

1 TIBS - LITIS - Equipe Traitement de l'information en Biologie Santé
2 LIMICS - Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé
3 Insiliance
4 D2IM - Département d'Informatique Médicale

In the context of the IA.TROMED project we intend to develop and evaluate original algorithmic methods that will rely on semantic enrichment of embeddings by combining new deep learning algorithms, such as models founded on transformers, and symbolic artificial intelligence. The documents’ embeddings, the graphs’ embeddings of biomedical concepts, and patients’ embeddings, all of them semantically enriched with aligned formal ontologies and semantic networks, will constitute a layer that will play the role of a queryable and searchable knowledge base that will supply the IA.TROMED’s clinical, predictive, and iatrogenic diagnosis support module.

Mots clés: Biomedical Ontologies; Knowledge Bases; Neural networks

Domaines: Informatique [cs]; Intelligence artificielle [cs.AI]

Lina F. Soualmia, Vincent Lafon, Stéfan Darmoni. Patient and Graph Embeddings for Predictive Diagnosis of Drug Iatrogenesis. Studies in Health Technology and Informatics, 2021, 281, pp.482-483. ⟨10.3233/SHTI210205⟩. ⟨hal-03243061⟩