Model Reduction of Shallow CNN Model for Reliable Deployment of Information Extraction from Medical Reports
This work addresses the need for reliable and interpretable AI deployment in medical information extraction, though it is incremental as it builds on existing shallow CNN methods.
The study tackled the problem of interpreting shallow CNN models for extracting information from cancer pathology reports by developing a model reduction tool that approximates CNN representations with linear transformations of n-gram features, achieving improved explainability without compromising accuracy.
Shallow Convolution Neural Network (CNN) is a time-tested tool for the information extraction from cancer pathology reports. Shallow CNN performs competitively on this task to other deep learning models including BERT, which holds the state-of-the-art for many NLP tasks. The main insight behind this eccentric phenomenon is that the information extraction from cancer pathology reports require only a small number of domain-specific text segments to perform the task, thus making the most of the texts and contexts excessive for the task. Shallow CNN model is well-suited to identify these key short text segments from the labeled training set; however, the identified text segments remain obscure to humans. In this study, we fill this gap by developing a model reduction tool to make a reliable connection between CNN filters and relevant text segments by discarding the spurious connections. We reduce the complexity of shallow CNN representation by approximating it with a linear transformation of n-gram presence representation with a non-negativity and sparsity prior on the transformation weights to obtain an interpretable model. Our approach bridge the gap between the conventionally perceived trade-off boundary between accuracy on the one side and explainability on the other by model reduction.