Classification of Pulmonary Nodular Findings based on Characterization of Change using Radiology Reports.

Radiology reports contain descriptions of radiological observations followed by diagnosis and follow up recommendations, transcribed by radiologists while reading medical images. One of the most challenging tasks in a radiology workflow is to extract, characterize and structure such content to be able to pair each observation with an appropriate action. This requires classification of the findings based on the provided characterization. In most clinical setups, this is done manually, which is tedious, time-consuming and prone to human error yet of great importance as various types of findings in the reports require different follow-up decision supports and draw different levels of attention. In this work, we present a framework for detection and classification of change characteristics of pulmonary nodular findings in radiology reports. We combine a pre-trained word embedding model with a deep learning based sentence encoder. To overcome the challenge of access to limited labeled data for training, we apply Siamese network with pairwise inputs, which enforces the similarities between findings under the same category. The proposed multitask neural network classifier was evaluated and compared against state-of-the-art approaches and demonstrated promising performance.