ArticleBMC bioinformatics2018
Using natural language processing and machine learning to identify breast cancer local recurrence.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 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
- Large language model ensemble for automated TNM staging from radiology reports.Bioinformatics (Oxford, England) · 2026Article
- Detection of cancer recurrence from Thai-English electronic medical records using sentence embeddings.BMJ health & care informatics · 2026Article
- Monte Carlo Tree Search for optimal cancer intervention strategies among BRCA mutation carriers.Computers in biology and medicine · 2025Article
- Natural language processing for local, regional, and distant breast cancer relapse identification in pathology reports.Breast cancer research and treatment · 2025Article
- Identification of M1 macrophage infiltration-related genes for immunotherapy in Her2-positive breast cancer based on bioinformatics analysis and machine learning.Scientific reports · 2025Article
- Decoding Recurrence in Early-Stage and Locoregionally Advanced Non-Small Cell Lung Cancer: Insights From Electronic Health Records and Natural Language Processing.JCO clinical cancer informatics · 2025Article
- Automated Identification of Breast Cancer Relapse in Computed Tomography Reports Using Natural Language Processing.JCO clinical cancer informatics · 2024Article
- Northwestern University resource and education development initiatives to advance collaborative artificial intelligence across the learning health system.Learning health systems · 2024Article
- Natural language processing to identify lupus nephritis phenotype in electronic health records.BMC medical informatics and decision making · 2024Article
- Development of an Automatic Rule-Based Algorithm for the Detection of Ovarian Cancer Recurrence From Electronic Health Records.JCO clinical cancer informatics · 2024Article
- Evaluation of Algorithms Using Automated Health Plan Data to Identify Breast Cancer Recurrences.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2024Article
- AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease.Journal of biomedical informatics · 2023Article
- Article
- Natural Language Processing for Breast Imaging: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Estimating the incidence of breast cancer recurrence using administrative data.Breast cancer research and treatment · 2023Article
- Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains.Diagnostics (Basel, Switzerland) · 2023Review
- How Natural Language Processing Can Aid With Pulmonary Oncology Tumor Node Metastasis Staging From Free-Text Radiology Reports: Algorithm Development and Validation.JMIR formative research · 2023Article
- A Systematic Review of Application Progress on Machine Learning-Based Natural Language Processing in Breast Cancer over the Past 5 Years.Diagnostics (Basel, Switzerland) · 2023Review
- Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundIdentifying local recurrences in breast cancer from patient data sets is important for clinical research and practice. Developing a model using natural language processing and machine learning to identify local recurrences in breast cancer patients can reduce the time-consuming work of a manual chart review.
methodsWe design a novel concept-based filter and a prediction model to detect local recurrences using EHRs. In the training dataset, we manually review a development corpus of 50 progress notes and extract partial sentences that indicate breast cancer local recurrence. We process these partial sentences to obtain a set of Unified Medical Language System (UMLS) concepts using MetaMap, and we call it positive concept set. We apply MetaMap on patients' progress notes and retain only the concepts that fall within the positive concept set. These features combined with the number of pathology reports recorded for each patient are used to train a support vector machine to identify local recurrences.
resultsWe compared our model with three baseline classifiers using either full MetaMap concepts, filtered MetaMap concepts, or bag of words. Our model achieved the best AUC (0.93 in cross-validation, 0.87 in held-out testing).
conclusionsCompared to a labor-intensive chart review, our model provides an automated way to identify breast cancer local recurrences. We expect that by minimally adapting the positive concept set, this study has the potential to be replicated at other institutions with a moderately sized training dataset.
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