Adapting and evaluating a deep learning language model for clinical why-question answering

Abstract Objectives To adapt and evaluate a deep learning language model for answering why-questions based on patient-specific clinical text. Materials and Methods Bidirectional encoder representations from transformers (BERT) models were trained with varying data sources to perform SQuAD 2.0 style why-question answering (why-QA) on clinical notes. The evaluation focused on: (1) comparing the merits from different training data and (2) error analysis. Results The best model achieved an accuracy of 0.707 (or 0.760 by partial match). Training toward customization for the clinical language helped increase 6% in accuracy. Discussion The error analysis suggested that the model did not really perform deep reasoning and that clinical why-QA might warrant more sophisticated solutions. Conclusion The BERT model achieved moderate accuracy in clinical why-QA and should benefit from the rapidly evolving technology. Despite the identified limitations, it could serve as a competent proxy for question-driven clinical information extraction.