ArticleJournal of the American Medical Informatics Association : JAMIA
Named entity recognition of follow-up and time information in 20,000 radiology reports.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Harnessing big data and artificial intelligence in transfusion medicine: Opportunities for precision, safety and efficiency.Vox sanguinis · 2026Review
- Automatic detection of actionable radiology reports using bidirectional encoder representations from transformers.BMC medical informatics and decision making · 2021Article
- Automated Detection of Radiology Reports that Require Follow-up Imaging Using Natural Language Processing Feature Engineering and Machine Learning Classification.Journal of digital imaging · 2020Article
- Interactive NLP in Clinical Care: Identifying Incidental Findings in Radiology Reports.Applied clinical informatics · 2019Article
- Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification.Artificial intelligence in medicine · 2019Article
- Use of Machine Learning to Identify Follow-Up Recommendations in Radiology Reports.Journal of the American College of Radiology : JACR · 2019Article
- Automated annotation and classification of BI-RADS assessment from radiology reports.Journal of biomedical informatics · 2017Article
- Speculation detection for Chinese clinical notes: Impacts of word segmentation and embedding models.Journal of biomedical informatics · 2016Article
- Joint segmentation and named entity recognition using dual decomposition in Chinese discharge summaries.Journal of the American Medical Informatics Association : JAMIA · 2014Article
- Electronic health records-driven phenotyping: challenges, recent advances, and perspectives.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- Semi-supervised clinical text classification with Laplacian SVMs: an application to cancer case management.Journal of biomedical informatics · 2013Article
- Using statistical text classification to identify health information technology incidents.Journal of the American Medical Informatics Association : JAMIAArticle
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Authors and funding
3 authors.
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Abstract
objectiveTo develop a system to extract follow-up information from radiology reports. The method may be used as a component in a system which automatically generates follow-up information in a timely fashion.
methodsA novel method of combining an LSP (labeled sequential pattern) classifier with a CRF (conditional random field) recognizer was devised. The LSP classifier filters out irrelevant sentences, while the CRF recognizer extracts follow-up and time phrases from candidate sentences presented by the LSP classifier. MEASUREMENTS: The standard performance metrics of precision (P), recall (R), and F measure (F) in the exact and inexact matching settings were used for evaluation.
resultsFour experiments conducted using 20,000 radiology reports showed that the CRF recognizer achieved high performance without time-consuming feature engineering and that the LSP classifier further improved the performance of the CRF recognizer. The performance of the current system is P=0.90, R=0.86, F=0.88 in the exact matching setting and P=0.98, R=0.93, F=0.95 in the inexact matching setting.
conclusionThe experiments demonstrate that the system performs far better than a baseline rule-based system and is worth considering for deployment trials in an alert generation system. The LSP classifier successfully compensated for the inherent weakness of CRF, that is, its inability to use global information.
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