Adapting and evaluating a deep learning language model for clinical why-question answering.
JAMIA Open
; 3(1): 16-20, 2020 Apr.
Article
em En
| MEDLINE
| ID: mdl-32607483
ABSTRACT
OBJECTIVES:
To adapt and evaluate a deep learning language model for answering why-questions based on patient-specific clinical text. MATERIALS ANDMETHODS:
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article