Poster: BioFactCheck: Exploring the Feasibility of Explainable Automated Inconsistency Detection in Biomedical and Health Literature - Lamichhane, Prajwol; Kahanda, Indika; Liu, Xudong; Umapathy, Karthikeyan; Reddivari, Sandeep; Christie, Catherine; Arikawa, Andrea; Ross, Jenifer (IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (Online), 2023)
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.