Summary of Deep Learning: a Heuristic Three-stage Mechanism For Grid Searches to Optimize the Future Risk Prediction Of Breast Cancer Metastasis Using Ehr-based Clinical Data, by Xia Jiang et al.
Deep Learning: a Heuristic Three-stage Mechanism for Grid Searches to Optimize the Future Risk Prediction of Breast Cancer Metastasis Using EHR-based Clinical Data
by Xia Jiang, Yijun Zhou, Chuhan Xu, Adam Brufsky, Alan Wells
First submitted to arxiv on: 14 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 The authors propose a heuristic three-stage mechanism for managing the running time of low-budget grid searches, which can optimize the prediction performance of deep learning models. They develop deep feedforward neural network (DFNN) models and conduct eight cycles of grid searches using their proposed strategies, including SSGS and RGS. The results show that grid search can significantly improve model prediction performance for predicting 5-year, 10-year, and 15-year risk of breast cancer metastasis. The authors also conduct SHAP analyses to interpret the importance of DFNN-model hyperparameters and clinical risk factors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses deep learning models to predict the risk of breast cancer metastasis after 5-15 years. To find the best model, researchers used a technique called grid search, which can be time-consuming. The authors developed a new way to manage this process, making it more efficient and effective. They also tested different strategies to improve the prediction accuracy. The results show that using grid search can lead to better predictions, with an improvement of up to 18.6% for certain scenarios. |
Keywords
» Artificial intelligence » Deep learning » Grid search » Neural network
