Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering (2020.lrec-1)
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| Challenge: | Existing work on EHR QA models that learn to answer questions from structured data has focused on analyzing questions or mapping questions to existing NLP based information extraction models. |
| Approach: | They conduct 48 experiments on two clinical question answering datasets . they use open-domain and domain-specific corpora to fine-tune Transformer language models . |
| Outcome: | The proposed models can learn to answer questions from unstructured notes with accuracies up to 90% on open-domain and domain-specific corpora. |
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