A Framework to Build A Causal Knowledge Graph for Chronic Diseases and Cancers by Discovering Semantic Associations from Biomedical Literature

Ali Daowd, Michael Barrett, Samina Abidi, Syed Sibte Raza Abidi

Résultat de recherche: Conference contribution

8 Citations (Scopus)

Résumé

Extracting knowledge from disparate biomedical literature can play an important role in the discovery of disease mechanisms and remedial therapies. This paper explores a hybrid semantics-based knowledge synthesis and discovery methodology that integrates approaches from Literature Based Discovery (LBD), Systems Medicine, and Knowledge Graphs to analyze published biomedical literature and discover potential causal associations between risk factors and Non-Communicable Diseases (NCDs). This paper presents a knowledge synthesis and discovery framework to (a) mine biomedical literature to identify semantic associations between risk factors and NCDs, and (b) represent them as a knowledge graph that outlines the multi-causal associations between underlying risk factors and NCDs. We employ a novel ranking algorithm that considers direct and indirect relation-based methods, augmented by semantic relatedness, to discover causal associations between risk factors and a targeted condition - in this case breast cancer. The novelty of our work is the use of breast cancer-specific embeddings in combination with graph-based metrics to quantitatively evaluate semantic association based on causality. We evaluate the performance of our breast cancer-specific word embedding model by utilizing information retrieval methods and manually curated breast cancer relations. Results confirm that (a) our cancer-specific word embedding model out-performs non-disease-specific models with respect to retrieval of breast cancer relations, and (b) our method generates valid causal knowledge about causal risk and protective factors related to breast cancer. Our present study focuses on breast cancer, however, our method is adaptable to other NCDs.

Langue d'origineEnglish
Titre de la publication principaleProceedings - 2021 IEEE 9th International Conference on Healthcare Informatics, ISCHI 2021
Maison d'éditionInstitute of Electrical and Electronics Engineers Inc.
Pages13-22
Nombre de pages10
ISBN (électronique)9781665401326
DOI
Statut de publicationPublished - août 2021
Événement9th IEEE International Conference on Healthcare Informatics, ISCHI 2021 - Virtual, Victoria, Canada
Durée: août 9 2021août 12 2021

Séries de publication

PrénomProceedings - 2021 IEEE 9th International Conference on Healthcare Informatics, ISCHI 2021

Conference

Conference9th IEEE International Conference on Healthcare Informatics, ISCHI 2021
Pays/TerritoireCanada
VilleVirtual, Victoria
Période8/9/218/12/21

Note bibliographique

Publisher Copyright:
© 2021 IEEE.

ASJC Scopus Subject Areas

  • Modelling and Simulation
  • Health Informatics
  • Health(social science)

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Citer

Daowd, A., Barrett, M., Abidi, S., & Abidi, S. S. R. (2021). A Framework to Build A Causal Knowledge Graph for Chronic Diseases and Cancers by Discovering Semantic Associations from Biomedical Literature. Dans Proceedings - 2021 IEEE 9th International Conference on Healthcare Informatics, ISCHI 2021 (pp. 13-22). (Proceedings - 2021 IEEE 9th International Conference on Healthcare Informatics, ISCHI 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI52183.2021.00016