Name
Title | ||
|---|---|---|
Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation. |
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.18653/v1/D19-6214 | LOUHI@EMNLP |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander Te-Wei Shieh | 1 | 0 | 0.34 |
| Yung-Sung Chuang | 2 | 14 | 3.74 |
| Shang-Yu Su | 3 | 9 | 4.88 |
| Yun-Nung Chen | 4 | 324 | 35.41 |