Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation

Alexander Te-Wei Shieh, Yung-Sung Chuang, Shang-Yu Su, Yun-Nung Chen


Abstract
Randomized controlled trials (RCTs) represent the paramount evidence of clinical medicine. Using machines to interpret the massive amount of RCTs has the potential of aiding clinical decision-making. We propose a RCT conclusion generation task from the PubMed 200k RCT sentence classification dataset to examine the effectiveness of sequence-to-sequence models on understanding RCTs. We first build a pointer-generator baseline model for conclusion generation. Then we fine-tune the state-of-the-art GPT-2 language model, which is pre-trained with general domain data, for this new medical domain task. Both automatic and human evaluation show that our GPT-2 fine-tuned models achieve improved quality and correctness in the generated conclusions compared to the baseline pointer-generator model. Further inspection points out the limitations of this current approach and future directions to explore.
Anthology ID:
D19-6214
Volume:
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
Month:
November
Year:
2019
Address:
Hong Kong
Venues:
EMNLP | Louhi | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
108–117
Language:
URL:
https://www.aclweb.org/anthology/D19-6214
DOI:
10.18653/v1/D19-6214
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-6214.pdf