@inproceedings{shieh-etal-2019-towards,
title = "Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation",
author = "Shieh, Alexander Te-Wei and
Chuang, Yung-Sung and
Su, Shang-Yu and
Chen, Yun-Nung",
editor = "Holderness, Eben and
Jimeno Yepes, Antonio and
Lavelli, Alberto and
Minard, Anne-Lyse and
Pustejovsky, James and
Rinaldi, Fabio",
booktitle = "Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)",
month = nov,
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-6214/",
doi = "10.18653/v1/D19-6214",
pages = "108--117",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation
%A Shieh, Alexander Te-Wei
%A Chuang, Yung-Sung
%A Su, Shang-Yu
%A Chen, Yun-Nung
%Y Holderness, Eben
%Y Jimeno Yepes, Antonio
%Y Lavelli, Alberto
%Y Minard, Anne-Lyse
%Y Pustejovsky, James
%Y Rinaldi, Fabio
%S Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F shieh-etal-2019-towards
%X 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.
%R 10.18653/v1/D19-6214
%U https://aclanthology.org/D19-6214/
%U https://doi.org/10.18653/v1/D19-6214
%P 108-117
Markdown (Informal)
[Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation](https://aclanthology.org/D19-6214/) (Shieh et al., Louhi 2019)
- Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation (Shieh et al., Louhi 2019)
ACL
- Alexander Te-Wei Shieh, Yung-Sung Chuang, Shang-Yu Su, and Yun-Nung Chen. 2019. Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation. In Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019), pages 108–117, Hong Kong. Association for Computational Linguistics.