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| http://dx.doi.org/10.1136/amiajnl-2013-001669 | DOI Listing |
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Named entity recognition for de-identifying Spanish electronic health records.
Comput Biol Med
December 2024
Departamento de Lenguajes y Ciencias de la Computación, Escuela Técnica Superior de Ingeniería Informática, Universidad de Málaga, Málaga, Spain; Research Institute of Multilingual Language Technologies, Universidad de Málaga, Málaga, Spain.
Background And Objectives: There is an increasing and renewed interest in Electronic Health Records (EHRs) as a substantial information source for clinical decision making. Consequently, automatic de-identification of EHRs is an indispensable task, since their dissociation from personal data is a necessary prerequisite for their dissemination. Nevertheless, the bulk of prior research in this domain has been conducted using English EHRs, given the limited availability of annotated corpora in other languages, including Spanish.
View Article and Find Full Text PDFInvestigational Use of Real-World Data as a Hybrid Control in Pancreatic Ductal Adenocarcinoma from the Randomized Phase Ib/II MORPHEUS Trial.
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Cancer Center Clinica Universidad de Navarra and Program in Solid Tumors (CIMA), Universidad de Navarra, IDISNA, Pamplona, Spain.
Enrolling adequate numbers of patients into the control arm of randomized controlled trials (RCTs) often presents barriers. There is interest in leveraging real-world data (RWD) from electronic health records (EHRs) to construct external control (EC) arms to supplement RCT control arms and form hybrid control (HC) arms. This investigation showed the use of an HC arm in second-line metastatic pancreatic ductal adenocarcinoma (PDAC).
View Article and Find Full Text PDFEvaluating the Impact of Artificial Intelligence (AI) on Clinical Documentation Efficiency and Accuracy Across Clinical Settings: A Scoping Review.
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General Internal Medicine, University Hospitals Plymouth NHS Trust, Plymouth, GBR.
Artificial intelligence (AI) technologies (natural language processing (NLP), speech recognition (SR), and machine learning (ML)) can transform clinical documentation in healthcare. This scoping review evaluates the impact of AI on the accuracy and efficiency of clinical documentation across various clinical settings (hospital wards, emergency departments, and outpatient clinics). We found 176 articles by applying a specific search string on Ovid.
View Article and Find Full Text PDFFine-grained Patient Similarity Measuring using Contrastive Graph Similarity Networks.
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School of Computing Technologies, RMIT University, Melbourne, VIC, Australia.
Predictive analytics using Electronic Health Records (EHRs) have become an active research area in recent years, especially with the development of deep learning techniques. A popular EHR data analysis paradigm in deep learning is patient representation learning, which aims to learn a condensed mathematical representation of individual patients. However, EHR data are often inherently irregular, i.
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Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada.
Background: In light of the complex and high-risk nature of parenteral nutrition (PN), reviewing PN processing steps is essential to minimize patient harm. The main steps include ordering, verification, compounding, and administration. Electronic health records (EHRs) have become increasingly utilized and can play a critical role in enhancing the safety of PN processin.
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