Natural language processing data services for healthcare providers
Abstract 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 qu...
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| Format: | Article |
| Language: | English |
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BMC
2024-11-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-024-02713-x |
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| _version_ | 1846147758052868096 |
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| author | Joshua Au Yeung Anthony Shek Thomas Searle Zeljko Kraljevic Vlad Dinu Mart Ratas Mohammad Al-Agil Aleksandra Foy Barbara Rafferty Vitaliy Oliynyk James T. Teo |
| author_facet | Joshua Au Yeung Anthony Shek Thomas Searle Zeljko Kraljevic Vlad Dinu Mart Ratas Mohammad Al-Agil Aleksandra Foy Barbara Rafferty Vitaliy Oliynyk James T. Teo |
| author_sort | Joshua Au Yeung |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-8cf525a2371940b88626bd4773b8c30e |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-8cf525a2371940b88626bd4773b8c30e2024-12-01T12:29:16ZengBMCBMC Medical Informatics and Decision Making1472-69472024-11-0124111010.1186/s12911-024-02713-xNatural language processing data services for healthcare providersJoshua Au Yeung0Anthony Shek1Thomas Searle2Zeljko Kraljevic3Vlad Dinu4Mart Ratas5Mohammad Al-Agil6Aleksandra Foy7Barbara Rafferty8Vitaliy Oliynyk9James T. Teo10CogStack, Guys and St Thomas NHS TrustCogStack, Guys and St Thomas NHS TrustCogStack, Guys and St Thomas NHS TrustDepartment of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College LondonDepartment of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College LondonDepartment of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College LondonDepartment of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College LondonCogStack, Guys and St Thomas NHS TrustCogStack, Guys and St Thomas NHS TrustCogStack, Guys and St Thomas NHS TrustCogStack, Guys and St Thomas NHS TrustAbstract 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.https://doi.org/10.1186/s12911-024-02713-xNatural language processingLarge language modelsBioinformaticsMachine learningElectronic health records |
| spellingShingle | Joshua Au Yeung Anthony Shek Thomas Searle Zeljko Kraljevic Vlad Dinu Mart Ratas Mohammad Al-Agil Aleksandra Foy Barbara Rafferty Vitaliy Oliynyk James T. Teo Natural language processing data services for healthcare providers BMC Medical Informatics and Decision Making Natural language processing Large language models Bioinformatics Machine learning Electronic health records |
| title | Natural language processing data services for healthcare providers |
| title_full | Natural language processing data services for healthcare providers |
| title_fullStr | Natural language processing data services for healthcare providers |
| title_full_unstemmed | Natural language processing data services for healthcare providers |
| title_short | Natural language processing data services for healthcare providers |
| title_sort | natural language processing data services for healthcare providers |
| topic | Natural language processing Large language models Bioinformatics Machine learning Electronic health records |
| url | https://doi.org/10.1186/s12911-024-02713-x |
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