Poster: BioFactCheck: Exploring the Feasibility of Explainable Automated Inconsistency Detection in Biomedical and Health Literature

There are inconsistencies in conclusions drawn from the studies that address the same research question in the biomedical literature. This paper presents preliminary work on the approaches taken to build an inconsistency detection and explanation model starting with the development of a gold-standar...

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Published in:IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (Online) pp. 196 - 197
Main Authors: Lamichhane, Prajwol, Kahanda, Indika, Liu, Xudong, Umapathy, Karthikeyan, Reddivari, Sandeep, Christie, Catherine, Arikawa, Andrea, Ross, Jenifer
Format: Conference Proceeding
Language:English
Published: ACM 01.06.2023
Subjects:
Biological system modeling
biomedical literature
contradiction detection
SemRep
ISSN:2832-2975
Online Access:Get full text
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Abstract There are inconsistencies in conclusions drawn from the studies that address the same research question in the biomedical literature. This paper presents preliminary work on the approaches taken to build an inconsistency detection and explanation model starting with the development of a gold-standard contradiction sentences corpus. First, we utilize SemRep, a third-party tool that can automatically segment any biomedical sentence into the form of a subject, predicate, and object. A pair of sentences with the same subject/object but different predicates is identified as contradictory sentences. These sentences are then manually curated by domain experts to filter out noise. In the future, we plan to generate a large manually curated gold-standard contradiction sentence dataset and use that for developing an automated tool for detecting and extracting contradictions in biomedical and health text.
AbstractList There are inconsistencies in conclusions drawn from the studies that address the same research question in the biomedical literature. This paper presents preliminary work on the approaches taken to build an inconsistency detection and explanation model starting with the development of a gold-standard contradiction sentences corpus. First, we utilize SemRep, a third-party tool that can automatically segment any biomedical sentence into the form of a subject, predicate, and object. A pair of sentences with the same subject/object but different predicates is identified as contradictory sentences. These sentences are then manually curated by domain experts to filter out noise. In the future, we plan to generate a large manually curated gold-standard contradiction sentence dataset and use that for developing an automated tool for detecting and extracting contradictions in biomedical and health text.
Author Lamichhane, Prajwol
Reddivari, Sandeep
Arikawa, Andrea
Kahanda, Indika
Christie, Catherine
Ross, Jenifer
Liu, Xudong
Umapathy, Karthikeyan
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  organization: University of North FLorida,Jacksonville,Florida,USA
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Snippet There are inconsistencies in conclusions drawn from the studies that address the same research question in the biomedical literature. This paper presents...
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SubjectTerms Biological system modeling
biomedical literature
contradiction detection
SemRep
Title Poster: BioFactCheck: Exploring the Feasibility of Explainable Automated Inconsistency Detection in Biomedical and Health Literature
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