ArticleJournal of biomedical informatics2013
Semi-supervised clinical text classification with Laplacian SVMs: an application to cancer case management.
Article in Journal of biomedical informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications.Abdominal radiology (New York) · 2025Review
- BERT-based Transfer Learning in Sentence-level Anatomic Classification of Free-Text Radiology Reports.Radiology. Artificial intelligence · 2023Article
- Biomedical Text Classification Using Augmented Word Representation Based on Distributional and Relational Contexts.Computational intelligence and neuroscience · 2023Article
- The role of artificial intelligence in paediatric neuroradiology.Pediatric radiology · 2022Review
- Accurately Identifying Cerebroarterial Stenosis from Angiography Reports Using Natural Language Processing Approaches.Diagnostics (Basel, Switzerland) · 2022Article
- Clinical Text Data Categorization and Feature Extraction Using Medical-Fissure Algorithm and Neg-Seq Algorithm.Computational intelligence and neuroscience · 2022Article
- TextNetTopics: Text Classification Based Word Grouping as Topics and Topics' Scoring.Frontiers in genetics · 2022Article
- Automatic detection of actionable radiology reports using bidirectional encoder representations from transformers.BMC medical informatics and decision making · 2021Article
- Raman Microspectroscopic Investigation and Classification of Breast Cancer Pathological Characteristics.Molecules (Basel, Switzerland) · 2021Article
- Instantaneous mental workload assessment using time-frequency analysis and semi-supervised learning.Cognitive neurodynamics · 2020Article
- A Web Application for Adrenal Incidentaloma Identification, Tracking, and Management Using Machine Learning.Applied clinical informatics · 2020Article
- 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
- A frame semantic overview of NLP-based information extraction for cancer-related EHR notes.Journal of biomedical informatics · 2019Review
- Clinical text classification with rule-based features and knowledge-guided convolutional neural networks.BMC medical informatics and decision making · 2019Article
- Natural language processing of radiology reports for identification of skeletal site-specific fractures.BMC medical informatics and decision making · 2019Article
- Natural Language Processing for Automated Quantification of Brain Metastases Reported in Free-Text Radiology Reports.JCO clinical cancer informatics · 2019Article
- Identifying Breast Cancer Distant Recurrences from Electronic Health Records Using Machine Learning.Journal of healthcare informatics research · 2019Article
- Clinical Document Classification Using Labeled and Unlabeled Data Across Hospitals.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Semi-supervised Learning for Phenotyping Tasks.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2015Article
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3 authors.
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Abstract
objectiveTo compare linear and Laplacian SVMs on a clinical text classification task; to evaluate the effect of unlabeled training data on Laplacian SVM performance.
backgroundThe development of machine-learning based clinical text classifiers requires the creation of labeled training data, obtained via manual review by clinicians. Due to the effort and expense involved in labeling data, training data sets in the clinical domain are of limited size. In contrast, electronic medical record (EMR) systems contain hundreds of thousands of unlabeled notes that are not used by supervised machine learning approaches. Semi-supervised learning algorithms use both labeled and unlabeled data to train classifiers, and can outperform their supervised counterparts.
methodsWe trained support vector machines (SVMs) and Laplacian SVMs on a training reference standard of 820 abdominal CT, MRI, and ultrasound reports labeled for the presence of potentially malignant liver lesions that require follow up (positive class prevalence 77%). The Laplacian SVM used 19,845 randomly sampled unlabeled notes in addition to the training reference standard. We evaluated SVMs and Laplacian SVMs on a test set of 520 labeled reports.
resultsThe Laplacian SVM trained on labeled and unlabeled radiology reports significantly outperformed supervised SVMs (Macro-F1 0.773 vs. 0.741, Sensitivity 0.943 vs. 0.911, Positive Predictive value 0.877 vs. 0.883). Performance improved with the number of labeled and unlabeled notes used to train the Laplacian SVM (pearson's ρ=0.529 for correlation between number of unlabeled notes and macro-F1 score). These results suggest that practical semi-supervised methods such as the Laplacian SVM can leverage the large, unlabeled corpora that reside within EMRs to improve clinical text classification.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.