Epidemiology Commons^™

Class Imbalance In Out-Of-Distribution Datasets: Improving The Robustness Of The Textcnn For The Classification Of Rare Cancer Types, Kevin De Angeli, Shang Gao, Ioana Danciu, Eric B. Durbin, Xiao Cheng Wu, Antoinette Stroup, Jennifer Doherty, Stephen Schwartz, Charles Wiggins, Mark Damesyn, Linda Coyle, Lynne Penberthy, Georgia D. Tourassi, Hong Jun Yoon

School of Public Health Faculty Publications

In the last decade, the widespread adoption of electronic health record documentation has created huge opportunities for information mining. Natural language processing (NLP) techniques using machine and deep learning are becoming increasingly widespread for information extraction tasks from unstructured clinical notes. Disparities in performance when deploying machine learning models in the real world have recently received considerable attention. In the clinical NLP domain, the robustness of convolutional neural networks (CNNs) for classifying cancer pathology reports under natural distribution shifts remains understudied. In this research, we aim to quantify and improve the performance of the CNN for text classification on out-of-distribution …