Natural Language Processing in Dutch Free Text Radiology Reports: Challenges in a Small Language Area Staging Pulmonary Oncology.
Reports are the standard way of communication between the radiologist and the referring clinician. Efforts are made to improve this communication by, for instance, introducing standardization and structured reporting. Natural Language Processing (NLP) is another promising tool which can improve and...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 4; pp. 1002 - 1009 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2020
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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=146122208&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146122208 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2020 vid: 33 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146122208 144059527 146122208 146122208 10.1007/s10278-020-00327-z 146122208 ppf: 1002 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Natural Language Processing in Dutch Free Text Radiology Reports: Challenges in a Small Language Area Staging Pulmonary Oncology. aug: au: Nobel, J. Martijn Puts, Sander Bakers, Frans C. H. Robben, Simon G. F. Dekker, André L. A. J. affil: Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, Postbox 5800, 6202, Maastricht, AZ, Netherlands sug: subj: Natural Language Processing Netherlands Reports Algorithms Lung Neoplasms Radiography Neoplasm Staging Human Machine Learning Sensitivity and Specificity Descriptive Statistics Pilot Studies Data Mining Netherlands Health Informatics ab: Reports are the standard way of communication between the radiologist and the referring clinician. Efforts are made to improve this communication by, for instance, introducing standardization and structured reporting. Natural Language Processing (NLP) is another promising tool which can improve and enhance the radiological report by processing free text. NLP as such adds structure to the report and exposes the information, which in turn can be used for further analysis. This paper describes pre-processing and processing steps and highlights important challenges to overcome in order to successfully implement a free text mining algorithm using NLP tools and machine learning in a small language area, like Dutch. A rule-based algorithm was constructed to classify T-stage of pulmonary oncology from the original free text radiological report, based on the items tumor size, presence and involvement according to the 8th TNM classification system. PyContextNLP, spaCy and regular expressions were used as tools to extract the correct information and process the free text. Overall accuracy of the algorithm for evaluating T-stage was 0,83 in the training set and 0,87 in the validation set, which shows that the approach in this pilot study is promising. Future research with larger datasets and external validation is needed to be able to introduce more machine learning approaches and perhaps to reduce required input efforts of domain-specific knowledge. However, a hybrid NLP approach will probably achieve the best results. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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