ArticleArtificial intelligence in medicine2020
Prediction of breast cancer distant recurrence using natural language processing and knowledge-guided convolutional neural network.
Article in Artificial intelligence in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evolution of Breast Cancer Recurrence Risk Prediction: A Systematic Review of Statistical and Machine Learning-Based Models.JCO clinical cancer informatics · 2023Pooled it
- Predicting breast cancer 5-year survival using machine learning: A systematic review.PloS one · 2021Pooled it
- Recurrence of Basal Cell Carcinoma across Different Treatment Modalities: A Nationwide Study with 8 Years of Follow-up and Modelled Prediction.Acta dermato-venereologica · 2026Article
- Integrating a Shareable Artificial Intelligence Model Into Clinical Research for Cancer Recurrence in Patients With Breast and Colorectal Cancer.JCO clinical cancer informatics · 2025Article
- Natural language processing for local, regional, and distant breast cancer relapse identification in pathology reports.Breast cancer research and treatment · 2025Article
- Deep learning for survival prediction in triple-negative breast cancer: development and validation in real-world cohorts.Scientific reports · 2025Article
- Predicting gastric cancer survival using machine learning: A systematic review.World journal of gastrointestinal oncology · 2025Article
- Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data.Discover oncology · 2025Review
- How to select predictive models for decision-making or causal inference.GigaScience · 2025Article
- Nanotechnology-Driven Treatment Strategies for Breast Cancer: Recent Advances and Innovations.Oncology research · 2025Review
- A survey of NLP methods for oncology in the past decade with a focus on cancer registry applications.Artificial intelligence review · 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
- Advancing equity in breast cancer care: natural language processing for analysing treatment outcomes in under-represented populations.BMJ health & care informatics · 2024Article
- Explanatory argumentation in natural language for correct and incorrect medical diagnoses.Journal of biomedical semantics · 2024Article
- Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing.Communications medicine · 2024Article
- Automatic Detection of Distant Metastasis Mentions in Radiology Reports in Spanish.JCO clinical cancer informatics · 2024Article
- Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form.PloS one · 2024Article
- Using the Electronic Health Record to Develop a Gastric Cancer Risk Prediction Model.Gastro hep advances · 2024Article
- An adsorption isotherm identification method based on CNN-LSTM neural network.Journal of molecular modeling · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Distant recurrence of breast cancer results in high lifetime risks and low 5-year survival rates. Early prediction of distant recurrent breast cancer could facilitate intervention and improve patients' life quality. In this study, we designed an EHR-based predictive model to estimate the distant recurrent probability of breast cancer patients. We studied the pathology reports and progress notes of 6,447 patients who were diagnosed with breast cancer at Northwestern Memorial Hospital between 2001 and 2015. Clinical notes were mapped to Concept unified identifiers (CUI) using natural language processing tools. Bag-of-words and pre-trained embedding were employed to vectorize words and CUI sequences. These features integrated with clinical features from structured data were downstreamed to conventional machine learning classifiers and Knowledge-guided Convolutional Neural Network (K-CNN). The best configuration of our model yielded an AUC of 0.888 and an F1-score of 0.5. Our work provides an automated method to predict breast cancer distant recurrence using natural language processing and deep learning approaches. We expect that through advanced feature engineering, better predictive performance could be achieved.
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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.