| Autor: |
Au Yeung, Joshua, Shek, Anthony, Searle, Thomas, Kraljevic, Zeljko, Dinu, Vlad, Ratas, Mart, Al-Agil, Mohammad, Foy, Aleksandra, Rafferty, Barbara, Oliynyk, Vitaliy, Teo, James T. |
| Zdroj: |
BMC Medical Informatics & Decision Making; 11/26/2024, Vol. 24 Issue 1, p1-10, 10p |
| Abstrakt: |
Purpose of Review: Embedding machine learning workflows into real-world hospital environments is essential to ensure model alignment with clinical workflows and real-world data. Many non-healthcare industries undergoing digital transformation have already developed data labelling and data quality management services as a vertically integrated business process. Recent Findings: In this paper, we describe our experiences developing and implementing a first-of-its-kind clinical NLP (natural language processing) service in the National Health Service, United Kingdom using parallel harmonised platforms. We report on our work developing clinical NLP resources and implementation framework to distil expert clinical knowledge into our NLP models. To date, we have amassed over 26,086 annotations spanning 556 SNOMED CT concepts working with secondary care specialties. Summary: Our integrated language modelling service has delivered numerous clinical and operational use-cases using named entity recognition (NER). Such services improve efficiency of healthcare delivery and drive downstream data-driven technologies. We believe it will only be a matter of time before NLP services become an integral part of healthcare providers. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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