  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, 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.
