Proximity-Aware Clinical Passage Retrieval Framework by Exploiting Knowledge Structure

• Published in: IEEE access Vol. 11; pp. 37681 - 37693
• Main Authors: Han, Keejun, Kim, Jungeun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biological system modeling; CDS search; clinical decision support; Decision making; Knowledge; knowledge structure; Laboratory tests; Mobile applications; Motion pictures; passage retrieval; Periodic structures; Probabilistic logic; Proximity; Query expansion; Retrieval; Searching; Unified modeling language
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3266004

Clinicians have minimal time to search for and absorb the information needed while performing the duties of their medical practice. Their time-pressured situations requires the relevant information in search to be retrieved and presented in a more succinct form, such as in a short passage, rather than a whole page or document. In this context, clinical decision support (CDS) searches are beneficial when used to retrieve critical medical passages that can assist the practice of medical experts by offering appropriate medical information relevant to the medical case at hand. We present a novel CDS search framework designed for passage retrieval in order to support clinical decision-making using laboratory test results by incorporating proximity information. To do so, we use a knowledge structure that graphically visualizes key concepts and the corresponding relationships in a specific domain where nodes denote with their associative relationships. Furthermore, unlike previous studies that exploit knowledge structures during a re-ranking step, only dealing with initially highly retrieved passages, we utilize a knowledge structure for the purpose of query expansion. By doing so, our approach can unveil passages that are not retrieved during the initial retrieval process by including latent terms in the query list. We compared two models with/without edge pruning to capture a more latent relationship between terms. Our experiment showed that the embedded-based knowledge structure outperformed previous knowledge structure building approaches and other proximity-aware state-of-the-art models.