Identification of hyperparameters with high effects on performance of deep neural networks: application to clinicopathological data of ovarian cancer Recent advances in deep learning have emerged as an effective approach for precision medicine. The applications of deep learning to medicine have been applied mainly to medical image data but not clinicopathological data. One of challenges of deep learning model to clinicopathological data is to optimize hyperparameters to get high predictive power. In this study, we identified hyperparameters of deep learning model that have large effects on power. Specifically, we focused on predicting platinumbased chemotherapy response for ovarian cancer patients. As a performance metric, we used the area under the curve. We optimized six hyperparameters: the number of hidden layers, number of hidden units, optimization algorithm, weight initialization, activation function, and dropout rate. We also identified significant interaction effects between hyperparameters. We successfully found the combination of hyperparameters having large effects on prediction. These optimal combinations are expected to increase the prediction accuracy for the response to chemotherapy for a variety of cancer patients.

Breast Cancer Classification using Deep Transfer Learning on Structured Healthcare Data Efforts to improve early identification of aggressive high grade breast cancers, which pose the greatest risk to patient health if not detected early, are hindered by the rarity of these events. To address this problem, we proposed an accurate and efficient deep transfer learning method to handle the imbalanced data problem that is prominent in breast cancer data. In contrast to existing approaches based primarily on large image databases, we focused on structured data, which has not been commonly used for deep transfer learning. We used a number of publicly available breast cancer data sets to generate a pretrained model and transfer learned concepts to predict high grade malignant tumors in patients diagnosed with breast cancer at Mayo Clinic. We compared our results with stateoftheart techniques for addressing the problem of imbalanced learning and confirmed the superiority of the proposed method. To further demonstrate the ability of the proposed method to handle different degrees of class imbalance, a series of experiments were performed on publicly available breast cancer data under simulated class imbalanced settings. Based on the experimental results, we concluded that the proposed deep transfer learning on structured data can be used as an efficient method to handle imbalanced class problems in clinical research.