ArticleJournal of healthcare informatics research2019
Identifying Breast Cancer Distant Recurrences from Electronic Health Records Using Machine Learning.
Article in Journal of healthcare informatics research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Risk prediction models for dysphagia after radiotherapy among patients with head and neck cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Predictive value of machine learning for breast cancer recurrence: a systematic review and meta-analysis.Journal of cancer research and clinical oncology · 2023Pooled it
- Detection of cancer recurrence from Thai-English electronic medical records using sentence embeddings.BMJ health & care informatics · 2026Article
- Natural language processing for local, regional, and distant breast cancer relapse identification in pathology reports.Breast cancer research and treatment · 2025Article
- Integrating a Shareable Artificial Intelligence Model Into Clinical Research for Cancer Recurrence in Patients With Breast and Colorectal Cancer.JCO clinical cancer informatics · 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
- Multimodal BEHRT: transformers for multimodal electronic health records to predict breast cancer prognosis.Frontiers in oncology · 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
- Performance assessment of hybrid machine learning approaches for breast cancer and recurrence prediction.PloS one · 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
- Natural Language Processing for Breast Imaging: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Over-the-Counter Breast Cancer Classification Using Machine Learning and Patient Registration Records.Diagnostics (Basel, Switzerland) · 2022Article
- Multimodal machine learning in precision health: A scoping review.NPJ digital medicine · 2022Article
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
- Prediction of breast cancer distant recurrence using natural language processing and knowledge-guided convolutional neural network.Artificial intelligence in medicine · 2020Article
- Preoperative magnetic resonance imaging use and oncologic outcomes in premenopausal breast cancer patients.NPJ breast cancer · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Accurately identifying distant recurrences in breast cancer from the Electronic Health Records (EHR) is important for both clinical care and secondary analysis. Although multiple applications have been developed for computational phenotyping in breast cancer, distant recurrence identification still relies heavily on manual chart review. In this study, we aim to develop a model that identifies distant recurrences in breast cancer using clinical narratives and structured data from EHR. We applied MetaMap to extract features from clinical narratives and also retrieved structured clinical data from EHR. Using these features, we trained a support vector machine model to identify distant recurrences in breast cancer patients. We trained the model using 1,396 double-annotated subjects and validated the model using 599 double-annotated subjects. In addition, we validated the model on a set of 4,904 single-annotated subjects as a generalization test. In the held-out test and generalization test, we obtained F-measure scores of 0.78 and 0.74, area under curve (AUC) scores of 0.95 and 0.93, respectively. To explore the representation learning utility of deep neural networks, we designed multiple convolutional neural networks and multilayer neural networks to identify distant recurrences. Using the same test set and generalizability test set, we obtained F-measure scores of 0.79 ± 0.02 and 0.74 ± 0.004, AUC scores of 0.95 ± 0.002 and 0.95 ± 0.01, respectively. Our model can accurately and efficiently identify distant recurrences in breast cancer by combining features extracted from unstructured clinical narratives and structured clinical data.
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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.