Deep learning to extract Breast Cancer diagnosis concepts
Oswaldo Solarte Pabón, María Torrente, Álvaro García-Barragán, Mariano Provencio, Ernestina Menasalvas, Vı́ctor Robles · 2022
The wide adoption of electronic health records (EHRs) provides a potential source to support clinical research. The Bidirectional Encoder Representations from Transformers (BERT) has shown promising results in extracting information in the biomedical domain, including the cancer field. However, one of the challenges in the cancer domain is annotating resources to support information extraction. In this paper, we will show how models trained in a lung cancer corpus can be used to extract cancer concepts even in other cancer types. In particular, we will show the performance of BERT models on breast cancer data that was not used to train the models. Results are very promising as they show the possibility of applying deep learning-based models to predict cancer concepts in a different dataset to the one they were trained on, representing a considerable save of time and resources.