ArticleHealthcare informatics research2016
Nomogram of Naive Bayesian Model for Recurrence Prediction of Breast Cancer.
Article in Healthcare informatics research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 4 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 4 syntheses or guidelines pooled 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
- Evolution of Breast Cancer Recurrence Risk Prediction: A Systematic Review of Statistical and Machine Learning-Based Models.JCO clinical cancer informatics · 2023Pooled it
- Predictive value of radiomics-based machine learning for the disease-free survival in breast cancer: a systematic review and meta-analysis.Frontiers in oncology · 2023Pooled it
- Prognostic models for breast cancer: a systematic review.BMC cancer · 2019Pooled it
- Use of artificial intelligence in breast surgery: a narrative review.Gland surgery · 2024Review
- A novel approach for transforming breast cancer stem cells into endothelial cells.Experimental and therapeutic medicine · 2024Article
- Machine learning-based model constructed from ultrasound radiomics and clinical features for predicting HER2 status in breast cancer patients with indeterminate (2+) immunohistochemical results.Cancer medicine · 2024Article
- Classification and prediction of cognitive trajectories of cognitively unimpaired individuals.Frontiers in aging neuroscience · 2023Article
- Application of Artificial Intelligence Techniques to Predict Risk of Recurrence of Breast Cancer: A Systematic Review.Journal of personalized medicine · 2022Review
- Influence of the COVID-19 Pandemic on the Subjective Life Satisfaction of South Korean Adults: Bayesian Nomogram Approach.Diagnostics (Basel, Switzerland) · 2022Article
- Development and Validation of a New Multiparametric Random Survival Forest Predictive Model for Breast Cancer Recurrence with a Potential Benefit to Individual Outcomes.Cancer management and research · 2022Article
- Nomogram for Predicting In-hospital Mortality in Infective Endocarditis Based on Early Clinical Features and Treatment Options.Frontiers in cardiovascular medicine · 2022Article
- Weakly supervised temporal model for prediction of breast cancer distant recurrence.Scientific reports · 2021Article
- Machine Learning Algorithms to Predict Recurrence within 10 Years after Breast Cancer Surgery: A Prospective Cohort Study.Cancers · 2020Article
- Prediction of breast cancer distant recurrence using natural language processing and knowledge-guided convolutional neural network.Artificial intelligence in medicine · 2020Article
- Developing Children's Oral Health Assessment Toolkits Using Machine Learning Algorithm.JDR clinical and translational research · 2020Article
- Prediction of Recurrence-associated Death from Localized Prostate Cancer with a Charlson Comorbidity Index-reinforced Machine Learning Model.Open medicine (Warsaw, Poland) · 2019Article
- DrKnow: A Diagnostic Learning Tool with Feedback from Automated Clinical Decision Support.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Feature Selection is Critical for 2-Year Prognosis in Advanced Stage High Grade Serous Ovarian Cancer by Using Machine Learning.Cancer control : journal of the Moffitt Cancer CenterArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesBreast cancer has a high rate of recurrence, resulting in the need for aggressive treatment and close follow-up. However, previously established classification guidelines, based on expert panels or regression models, are controversial. Prediction models based on machine learning show excellent performance, but they are not widely used because they cannot explain their decisions and cannot be presented on paper in the way that knowledge is customarily represented in the clinical world. The principal objective of this study was to develop a nomogram based on a naïve Bayesian model for the prediction of breast cancer recurrence within 5 years after breast cancer surgery.
methodsThe nomogram can provide a visual explanation of the predicted probabilities on a sheet of paper. We used a data set from a Korean tertiary teaching hospital of 679 patients who had undergone breast cancer surgery between 1994 and 2002. Seven prognostic factors were selected as independent variables for the model.
resultsThe accuracy was 80%, and the area under the receiver operating characteristics curve (AUC) of the model was 0.81.
conclusionsThe nomogram can be easily used in daily practice to aid physicians and patients in making appropriate treatment decisions after breast cancer surgery.
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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.