Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer.
Natural language processing (NLP), which aims to convert human language into expressions that can be analyzed by computers, is one of the most rapidly developing and widely used technologies in the field of artificial intelligence. Natural language processing algorithms convert unstructured free tex...
| Publicado en: | International Journal of Radiation Oncology, Biology, Physics Vol. 110; no. 3; pp. 641 - 656 |
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| Autores principales: | , , , |
| Formato: | review Journal Article |
| Publicado: |
Pergamon Press - An Imprint of Elsevier Science
Jul2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150639546&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150639546 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03603016 1ZQ jtl: International Journal of Radiation Oncology, Biology, Physics issn: 03603016 maglogo: N pubinfo: dt: Jul2021 vid: 110 iid: 3 pid: 2410 pub: Pergamon Press - An Imprint of Elsevier Science artinfo: ui: 150639546 150639546 NLM33545300 150639546 10.1016/j.ijrobp.2021.01.044 NLM33545300 150639546 ppf: 641 ppct: 15 formats: tig: atl: Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer. aug: au: Bitterman, Danielle S. Miller, Timothy A. Mak, Raymond H. Savova, Guergana K. affil: Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Boston, Massachusetts sug: subj: Radiation Oncology Natural Language Processing Scales ab: Natural language processing (NLP), which aims to convert human language into expressions that can be analyzed by computers, is one of the most rapidly developing and widely used technologies in the field of artificial intelligence. Natural language processing algorithms convert unstructured free text data into structured data that can be extracted and analyzed at scale. In medicine, this unlocking of the rich, expressive data within clinical free text in electronic medical records will help untap the full potential of big data for research and clinical purposes. Recent major NLP algorithmic advances have significantly improved the performance of these algorithms, leading to a surge in academic and industry interest in developing tools to automate information extraction and phenotyping from clinical texts. Thus, these technologies are poised to transform medical research and alter clinical practices in the future. Radiation oncology stands to benefit from NLP algorithms if they are appropriately developed and deployed, as they may enable advances such as automated inclusion of radiation therapy details into cancer registries, discovery of novel insights about cancer care, and improved patient data curation and presentation at the point of care. However, challenges remain before the full value of NLP is realized, such as the plethora of jargon specific to radiation oncology, nonstandard nomenclature, a lack of publicly available labeled data for model development, and interoperability limitations between radiation oncology data silos. Successful development and implementation of high quality and high value NLP models for radiation oncology will require close collaboration between computer scientists and the radiation oncology community. Here, we present a primer on artificial intelligence algorithms in general and NLP algorithms in particular; provide guidance on how to assess the performance of such algorithms; review prior research on NLP algorithms for oncology; and describe future avenues for NLP in radiation oncology research and clinics. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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