Informative Causality Extraction from Medical Literature via Dependency-Tree-Based Patterns

dc.contributor.authorKabir, M. Ahsanul
dc.contributor.authorAlmulhim, AlJohara
dc.contributor.authorLuo, Xiao
dc.contributor.authorAl Hasan, Mohammad
dc.contributor.departmentComputer Science, Luddy School of Informatics, Computing, and Engineering
dc.date.accessioned2024-05-15T13:07:22Z
dc.date.available2024-05-15T13:07:22Z
dc.date.issued2022-05-25
dc.description.abstractExtracting cause-effect entities from medical literature is an important task in medical information retrieval. A solution for solving this task can be used for compilation of various causality relations, such as causality between disease and symptoms, between medications and side effects, and between genes and diseases. Existing solutions for extracting cause-effect entities work well for sentences where the cause and the effect phrases are name entities, single-word nouns, or noun phrases consisting of two to three words. Unfortunately, in medical literature, cause and effect phrases in a sentence are not simply nouns or noun phrases, rather they are complex phrases consisting of several words, and existing methods fail to correctly extract the cause and effect entities in such sentences. Partial extraction of cause and effect entities conveys poor quality, non-informative, and often, contradictory facts, comparing to the one intended in the given sentence. In this work, we solve this problem by designing an unsupervised method for cause and effect phrase extraction, patterncausality, which is specifically suitable for the medical literature. Our proposed approach first uses a collection of cause-effect dependency patterns as template to extract head words of cause and effect phrases and then it uses a novel phrase extraction method to obtain complete and meaningful cause and effect phrases from a sentence. Experiments on a cause-effect dataset built from sentences from PubMed articles show that for extracting cause and effect entities, patterncausality is substantially better than the existing methods—with an order of magnitude improvement in the F-score metric over the best of the existing methods. We also build different variants of patterncausality, which use different phrase extraction methods; all variants are better than the existing methods. patterncausality and its variants also show modest performance improvement over the existing methods for extracting cause and effect entities in a domain-neutral benchmark dataset, in which cause and effect entities are nouns or noun phrases consisting of one to two words.
dc.eprint.versionFinal published version
dc.identifier.citationKabir MA, Almulhim A, Luo X, Al Hasan M. Informative Causality Extraction from Medical Literature via Dependency-Tree-Based Patterns. J Healthc Inform Res. 2022;6(3):295-316. Published 2022 May 25. doi:10.1007/s41666-022-00116-z
dc.identifier.urihttps://hdl.handle.net/1805/40762
dc.language.isoen_US
dc.publisherSpringer
dc.relation.isversionof10.1007/s41666-022-00116-z
dc.relation.journalJournal of Healthcare Informatics Research
dc.rightsPublisher Policy
dc.sourcePMC
dc.subjectCausality
dc.subjectCause-effect phrase extraction
dc.subjectCause-effect pattern
dc.titleInformative Causality Extraction from Medical Literature via Dependency-Tree-Based Patterns
dc.typeArticle
ul.alternative.fulltexthttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9131716/
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