Knowledge extraction based on linked open data for clinical documentation. (2018)

• Record Type: Journal Article
• Authors: Alobaidi, Mazen; Mahmood, Khalid; Sabra, Susan
• Is Part Of: International journal of simulation and process modelling. Volume 13:Number 2(2018)
• Page Start: 116
• Page End: 125
• Publication Date: 2018
• Subjects: linked open data -- LOD -- semantic web -- SPARQL -- Swoogle -- knowledge extraction -- clinical documentation; Management -- Computer simulation -- Periodicals; Mathematical models -- Periodicals; Operations research -- Periodicals; Simulation methods -- Periodicals; 003.05
• Languages: English
• ISSNs: 1740-2123

Abstract

Smart cities are becoming a reality in the near future to transform many sectors and activities in our lives. Smart cities systems such as healthcare systems will have new functionality to improve the quality of life. Electronic health records are an essential component of healthcare systems. They are valuable for medical research, but the information is recorded as unstructured free text. Knowledge extraction (KE) from unstructured text in electronic health records is a problem but still not totally resolved. KE is very challenging because medical language has ungrammatical and fragmented constructions. We have implemented a unique framework KE based on linked open data for clinical documentation (KE-LODC) that generates accurate and high quality triples transforming unstructured text from clinical documentation into well-defined and ready-to-use linked open data for diagnosis and treatment. Our framework proved to produce a large number of highly qualified triple candidates which improves the likelihood of better classification.