A semantically enriched text mining system for clinical decision support. (7th April 2020)

• Record Type: Journal Article
• Authors: Luque, Carmen; Luna, José M.; Ventura, Sebastián
• Other Names: Ventura Sebastian guestEditor.; Soda Paolo guestEditor.; González Alejandro Rodríguez guestEditor.
• Is Part Of: Computational intelligence. Volume 37:Number 4(2021)
• Page Start: 1545
• Page End: 1570
• Publication Date: 2020-04-07
• Subjects: classification -- clinical decision support system -- medical entities recognition -- MetaMap -- text mining; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12322
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Existing systems to support decision‐taking process based on textual information of clinical reports are insufficient. Currently, there are few systems that unify different subtasks in a single and user‐friendly framework, easing therefore the clinical work by automating complex and arduous tasks such as the detection of clinical alerts as well as clinical information coding. To address this issue, MiNerDoc is proposed as a new text mining (TM) system whose main objective is to support clinical decision‐taking processes by analyzing textual clinical reports in a unified framework. MiNerDoc is a really alluring TM system that includes two relevant tasks in the medical field, that is, detection of risk factors according to five medical entities (disease, pharmacologic, region/part body, procedure/test, and finding/sign) and automatic prediction of standardized diagnostic codes (MeSH descriptors associated with diseases). MiNerDoc integrates a combination of techniques from the TM discipline along with the terminological and semantic enrichment provided by the MetaMap tool and UMLS metathesaurus. Some study cases as well as a wide experimental analysis on real clinical reports have been carried out to demonstrate the effectiveness and promising performance of MiNerDoc on two different tasks, that is, medical entities recognition (FMeasure 81.54%) and diagnostic classification (FMeasuremic 81.04%).