ArticleJournal of the American Medical Informatics Association : JAMIA
Supervised machine learning and active learning in classification of 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 18 papers.
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Who cites it
18 citing papers in PubMed.
- Utilizing active learning strategies in machine-assisted annotation for clinical named entity recognition: a comprehensive analysis considering annotation costs and target effectiveness.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Dataset from a human-in-the-loop approach to identify functionally important protein residues from literature.Scientific data · 2024Article
- From data to insights: how natural language processing and structured reporting advance data-driven radiology.European radiology · 2023Article
- Multi-center Integrating Radiomics, Structured Reports, and Machine Learning Algorithms for Assisted Classification of COVID-19 in Lung Computed Tomography.Journal of medical and biological engineering · 2023Article
- Reviewing the relationship between machines and radiology: the application of artificial intelligence.Acta radiologica open · 2021Review
- Active deep learning for the identification of concepts and relations in electroencephalography reports.Journal of biomedical informatics · 2019Article
- Deep learning to convert unstructured CT pulmonary angiography reports into structured reports.European radiology experimental · 2019Article
- Weakly supervised natural language processing for assessing patient-centered outcome following prostate cancer treatment.JAMIA open · 2019Article
- Automatic inference of BI-RADS final assessment categories from narrative mammography report findings.Journal of biomedical informatics · 2019Article
- Radiology report annotation using intelligent word embeddings: Applied to multi-institutional chest CT cohort.Journal of biomedical informatics · 2018Article
- Interactive phenotyping of large-scale histology imaging data with HistomicsML.Scientific reports · 2017Article
- Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.Artificial intelligence in medicine · 2017Article
- Parallel multiple instance learning for extremely large histopathology image analysis.BMC bioinformatics · 2017Article
- Intelligent Word Embeddings of Free-Text Radiology Reports.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Improving condition severity classification with an efficient active learning based framework.Journal of biomedical informatics · 2016Article
- Classification of radiology reports for falls in an HIV study cohort.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- McTwo: a two-step feature selection algorithm based on maximal information coefficient.BMC bioinformatics · 2016Article
- Trends in biomedical informatics: automated topic analysis of JAMIA articles.Journal of the American Medical Informatics Association : JAMIA · 2015Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveThis paper presents an automated system for classifying the results of imaging examinations (CT, MRI, positron emission tomography) into reportable and non-reportable cancer cases. This system is part of an industrial-strength processing pipeline built to extract content from radiology reports for use in the Victorian Cancer Registry. MATERIALS AND
methodsIn addition to traditional supervised learning methods such as conditional random fields and support vector machines, active learning (AL) approaches were investigated to optimize training production and further improve classification performance. The project involved two pilot sites in Victoria, Australia (Lake Imaging (Ballarat) and Peter MacCallum Cancer Centre (Melbourne)) and, in collaboration with the NSW Central Registry, one pilot site at Westmead Hospital (Sydney).
resultsThe reportability classifier performance achieved 98.25% sensitivity and 96.14% specificity on the cancer registry's held-out test set. Up to 92% of training data needed for supervised machine learning can be saved by AL. DISCUSSION: AL is a promising method for optimizing the supervised training production used in classification of radiology reports. When an AL strategy is applied during the data selection process, the cost of manual classification can be reduced significantly.
conclusionsThe most important practical application of the reportability classifier is that it can dramatically reduce human effort in identifying relevant reports from the large imaging pool for further investigation of cancer. The classifier is built on a large real-world dataset and can achieve high performance in filtering relevant reports to support cancer registries.
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