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    <abstract>Clinical trials provide essential guidance for practicing Evidence-Based Medicine, though often accompanying with unendurable costs and risks. To optimize the design of clinical trials, we introduce a novel Clinical Trial Result Prediction (CTRP) task. In the CTRP framework, a model takes a PICO-formatted clinical trial proposal with its background as input and predicts the result, i.e. how the Intervention group compares with the Comparison group in terms of the measured Outcome in the studied Population. While structured clinical evidence is prohibitively expensive for manual collection, we exploit large-scale unstructured sentences from medical literature that implicitly contain PICOs and results as evidence. Specifically, we pre-train a model to predict the disentangled results from such implicit evidence and fine-tune the model with limited data on the downstream datasets. Experiments on the benchmark Evidence Integration dataset show that the proposed model outperforms the baselines by large margins, e.g., with a 10.7% relative gain over BioBERT in macro-F1. Moreover, the performance improvement is also validated on another dataset composed of clinical trials related to COVID-19.</abstract>
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%0 Conference Proceedings
%T Predicting Clinical Trial Results by Implicit Evidence Integration
%A Jin, Qiao
%A Tan, Chuanqi
%A Chen, Mosha
%A Liu, Xiaozhong
%A Huang, Songfang
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F jin-etal-2020-predicting
%X Clinical trials provide essential guidance for practicing Evidence-Based Medicine, though often accompanying with unendurable costs and risks. To optimize the design of clinical trials, we introduce a novel Clinical Trial Result Prediction (CTRP) task. In the CTRP framework, a model takes a PICO-formatted clinical trial proposal with its background as input and predicts the result, i.e. how the Intervention group compares with the Comparison group in terms of the measured Outcome in the studied Population. While structured clinical evidence is prohibitively expensive for manual collection, we exploit large-scale unstructured sentences from medical literature that implicitly contain PICOs and results as evidence. Specifically, we pre-train a model to predict the disentangled results from such implicit evidence and fine-tune the model with limited data on the downstream datasets. Experiments on the benchmark Evidence Integration dataset show that the proposed model outperforms the baselines by large margins, e.g., with a 10.7% relative gain over BioBERT in macro-F1. Moreover, the performance improvement is also validated on another dataset composed of clinical trials related to COVID-19.
%R 10.18653/v1/2020.emnlp-main.114
%U https://aclanthology.org/2020.emnlp-main.114
%U https://doi.org/10.18653/v1/2020.emnlp-main.114
%P 1461-1477

Markdown (Informal)

[Predicting Clinical Trial Results by Implicit Evidence Integration](https://aclanthology.org/2020.emnlp-main.114) (Jin et al., EMNLP 2020)

• Predicting Clinical Trial Results by Implicit Evidence Integration (Jin et al., EMNLP 2020)

ACL

• Qiao Jin, Chuanqi Tan, Mosha Chen, Xiaozhong Liu, and Songfang Huang. 2020. Predicting Clinical Trial Results by Implicit Evidence Integration. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1461–1477, Online. Association for Computational Linguistics.