Towards Understanding of Medical Randomized Controlled Trials by Conclusion Generation
Alexander Shieh, Yung-Sung Chuang, Shang‐Yu Su, Yun-Nung Chen · 2019
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 pointergenerator 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 * .