ArticleJournal of the American Medical Informatics Association : JAMIA
Automatic lymphoma classification with sentence subgraph mining from pathology 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 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies.Journal of biomedical semantics · 2020Pooled it
- A Natural Language Processing Framework for Structuring and Visualizing Clinical Trial Eligibility Criteria at Scale: Protocol for a Quantitative Study.JMIR research protocols · 2026Article
- Leveraging social media data to study disease and treatment characteristics of Hodgkin's lymphoma Using Natural Language Processing methods.PLOS digital health · 2025Article
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- A survey of NLP methods for oncology in the past decade with a focus on cancer registry applications.Artificial intelligence review · 2025Article
- Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system.Learning health systems · 2024Article
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
- An accessible, efficient, and accurate natural language processing method for extracting diagnostic data from pathology reports.Journal of pathology informatics · 2022Article
- Validation of deep learning natural language processing algorithm for keyword extraction from pathology reports in electronic health records.Scientific reports · 2020Article
- Development of a Novel Tool for the Retrieval and Analysis of Hormone Receptor Expression Characteristics in Metastatic Breast Cancer via Data Mining on Pathology Reports.BioMed research international · 2020Article
- Rich Text Formatted EHR Narratives: A Hidden and Ignored Trove.Studies in health technology and informatics · 2019Article
- Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review.JMIR medical informatics · 2019Review
- Analysis of Hormone Receptor Status in Primary and Recurrent Breast Cancer Via Data Mining Pathology Reports.Open medicine (Warsaw, Poland) · 2019Article
- Identifying Breast Cancer Distant Recurrences from Electronic Health Records Using Machine Learning.Journal of healthcare informatics research · 2019Article
- Using natural language processing and machine learning to identify breast cancer local recurrence.BMC bioinformatics · 2018Article
- Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2018Article
- Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Review
- OC-2-KB: A software pipeline to build an evidence-based obesity and cancer knowledge base.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2017Article
- Predicting Social Anxiety Treatment Outcome Based on Therapeutic Email Conversations.IEEE journal of biomedical and health informatics · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectivePathology reports are rich in narrative statements that encode a complex web of relations among medical concepts. These relations are routinely used by doctors to reason on diagnoses, but often require hand-crafted rules or supervised learning to extract into prespecified forms for computational disease modeling. We aim to automatically capture relations from narrative text without supervision.
methodsWe design a novel framework that translates sentences into graph representations, automatically mines sentence subgraphs, reduces redundancy in mined subgraphs, and automatically generates subgraph features for subsequent classification tasks. To ensure meaningful interpretations over the sentence graphs, we use the Unified Medical Language System Metathesaurus to map token subsequences to concepts, and in turn sentence graph nodes. We test our system with multiple lymphoma classification tasks that together mimic the differential diagnosis by a pathologist. To this end, we prevent our classifiers from looking at explicit mentions or synonyms of lymphomas in the text. RESULTS AND
conclusionsWe compare our system with three baseline classifiers using standard n-grams, full MetaMap concepts, and filtered MetaMap concepts. Our system achieves high F-measures on multiple binary classifications of lymphoma (Burkitt lymphoma, 0.8; diffuse large B-cell lymphoma, 0.909; follicular lymphoma, 0.84; Hodgkin lymphoma, 0.912). Significance tests show that our system outperforms all three baselines. Moreover, feature analysis identifies subgraph features that contribute to improved performance; these features agree with the state-of-the-art knowledge about lymphoma classification. We also highlight how these unsupervised relation features may provide meaningful insights into lymphoma classification.
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Registered trials
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.