A Frame-Based NLP System for Cancer-Related Information Extraction.

We propose a frame-based natural language processing (NLP) method that extracts cancer-related information from clinical narratives. We focus on three frames: cancer diagnosis, cancer therapeutic procedure, and tumor description. We utilize a deep learning-based approach, bidirectional Long Short-term Memory (LSTM) Conditional Random Field (CRF), which uses both character and word embeddings. The system consists of two constituent sequence classifiers: a frame identification (lexical unit) classifier and a frame element classifier. The classifier achieves an F1 of 93.70 for cancer diagnosis, 96.33 for therapeutic procedure, and 87.18 for tumor description. These represent improvements of 10.72, 0.85, and 8.04 over a baseline heuristic, respectively. Additionally, we demonstrate that the combination of both GloVe and MIMIC-III embeddings has the best representational effect. Overall, this study demonstrates the effectiveness of deep learning methods to extract frame semantic information from clinical narratives.