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
University of Pittsburgh（匹兹堡大学）；Pittsburgh VA Health System（匹兹堡退伍军人健康系统）；University of Pittsburgh School of Medicine（匹兹堡大学医学院）；UPMC Hillman Cancer Center（UPMC希尔曼癌症中心）
Xia Jiang, Yijun Zhou, Chuhan Xu, Adam Brufsky, Alan Wells
A grid search, at the cost of training and testing a large number of models, is an effective way to optimize the prediction performance of deep learning models. A challenging task concerning grid search is the time management. Without a good time management scheme, a grid search can easily be set off as a mission that will not finish in our lifetime. In this study, we introduce a heuristic three-stage mechanism for managing the running time of low-budget grid searches, and the sweet-spot grid search (SSGS) and randomized grid search (RGS) strategies for improving model prediction performance, in predicting the 5-year, 10-year, and 15-year risk of breast cancer metastasis. We develop deep feedforward neural network (DFNN) models and optimize them through grid searches. We conduct eight cycles of grid searches by applying our three-stage mechanism and SSGS and RGS strategies. We conduct various SHAP analyses including unique ones that interpret the importance of the DFNN-model hyperparameters. Our results show that grid search can greatly improve model prediction. The grid searches we conducted improved the risk prediction of 5-year, 10-year, and 15-year breast cancer metastasis by 18.6%, 16.3%, and 17.3% respectively, over the average performance of all corresponding models we trained using the RGS strategy. We not only demonstrate best model performance but also characterize grid searches from various aspects such as their capabilities of discovering decent models and the unit grid search time. The three-stage mechanism worked effectively. It made our low-budget grid searches feasible and manageable, and in the meantime helped improve model prediction performance. Our SHAP analyses identified both clinical risk factors important for the prediction of future risk of breast cancer metastasis, and DFNN-model hyperparameters important to the prediction of performance scores.
Deep Learning to Predict Late-Onset Breast Cancer Metastasis: the Single Hyperparameter Grid Search (SHGS) Strategy for Meta Tuning Concerning Deep Feed-forward Neural Network
Curvature-based Feature Selection with Application in Classifying Electronic Health Records
Durham University（杜伦大学）；Teesside University（提赛德大学）；Institute of Microelectronics, Chinese Academy of Sciences（中国科学院微电子研究所）；University of Chinese Academy of Sciences（中国科学院大学）
L1-regularized neural ranking for risk stratification and its application to prediction of time to distant metastasis in luminal node negative chemotherapy naïve breast cancer patients
Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida（健康结局与生物医学信息学系，佛罗里达大学医学院）；Division of Cardiovascular Medicine, Department of Medicine, College of Medicine, University of Florida（心血管医学部，医学院，佛罗里达大学）；Division of Hematology & Oncology, Department of Medicine, College of Medicine, University of Florida（血液学与肿瘤学部，医学院，佛罗里达大学）
