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        <title>Extraction of Diagnostic Reasoning Relations for Clinical Knowledge Graphs</title>
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            <namePart type="given">Samuel</namePart>
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    <abstract>Clinical knowledge graphs lack meaningful diagnostic relations (e.g. comorbidities, sign/symptoms), limiting their ability to represent real-world diagnostic processes. Previous methods in biomedical relation extraction have focused on concept relations, such as gene-disease and disease-drug, and largely ignored clinical processes. In this thesis, we leverage a clinical reasoning ontology and propose methods to extract such relations from a physician-facing point-of-care reference wiki and consumer health resource texts. Given the lack of data labeled with diagnostic relations, we also propose new methods of evaluating the correctness of extracted triples in the zero-shot setting. We describe a process for the intrinsic evaluation of new facts by triple confidence filtering and clinician manual review, as well extrinsic evaluation in the form of a differential diagnosis prediction task.</abstract>
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%0 Conference Proceedings
%T Extraction of Diagnostic Reasoning Relations for Clinical Knowledge Graphs
%A Socrates, Vimig
%Y Louvan, Samuel
%Y Madotto, Andrea
%Y Madureira, Brielen
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F socrates-2022-extraction
%X Clinical knowledge graphs lack meaningful diagnostic relations (e.g. comorbidities, sign/symptoms), limiting their ability to represent real-world diagnostic processes. Previous methods in biomedical relation extraction have focused on concept relations, such as gene-disease and disease-drug, and largely ignored clinical processes. In this thesis, we leverage a clinical reasoning ontology and propose methods to extract such relations from a physician-facing point-of-care reference wiki and consumer health resource texts. Given the lack of data labeled with diagnostic relations, we also propose new methods of evaluating the correctness of extracted triples in the zero-shot setting. We describe a process for the intrinsic evaluation of new facts by triple confidence filtering and clinician manual review, as well extrinsic evaluation in the form of a differential diagnosis prediction task.
%R 10.18653/v1/2022.acl-srw.33
%U https://aclanthology.org/2022.acl-srw.33
%U https://doi.org/10.18653/v1/2022.acl-srw.33
%P 413-421

Markdown (Informal)

[Extraction of Diagnostic Reasoning Relations for Clinical Knowledge Graphs](https://aclanthology.org/2022.acl-srw.33) (Socrates, ACL 2022)

• Extraction of Diagnostic Reasoning Relations for Clinical Knowledge Graphs (Socrates, ACL 2022)

ACL

• Vimig Socrates. 2022. Extraction of Diagnostic Reasoning Relations for Clinical Knowledge Graphs. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 413–421, Dublin, Ireland. Association for Computational Linguistics.