Summary of Coalitions Of Ai-based Methods Predict 15-year Risks Of Breast Cancer Metastasis Using Real-world Clinical Data with Auc Up to 0.9, by Xia Jiang et al.
Coalitions of AI-based Methods Predict 15-Year Risks of Breast Cancer Metastasis Using Real-World Clinical Data with AUC up to 0.9
by Xia Jiang, Yijun Zhou, Alan Wells, Adam Brufsky
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Quantitative Methods (q-bio.QM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning educators writing for technical audiences will appreciate this research paper’s abstract, which tackles the pressing issue of breast cancer prognosis. The study highlights the shortcomings of current prognostic metrics, which fail to provide actionable insights for most women seemingly cured after local treatment. As a result, many women receive unnecessary and potentially morbid adjuvant therapies. To address this challenge, researchers propose a data-driven approach using large datasets and machine learning algorithms to develop accurate predictive models. The study demonstrates the effectiveness of Bayesian Networks and grid search techniques in generating models with high AUC scores (up to 0.9) using only existing data. These findings have significant implications for clinical management, as they do not require additional testing beyond routine tumor evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Breast cancer is a major concern for women’s health, causing many deaths each year. While most breast cancers are diagnosed at an early stage and treated successfully, some women still develop metastatic disease later on. Current methods for predicting which women will relapse or remain cured are not very helpful. This means that many women receive treatments that don’t really help them, and some even receive treatments that can be harmful. To improve this situation, researchers looked at large datasets to see if they could identify patterns that would allow them to predict which women were likely to have their cancer come back. They developed special algorithms using machine learning techniques and found that these models were very accurate in predicting the outcome of breast cancer treatment. |
Keywords
» Artificial intelligence » Auc » Grid search » Machine learning
