# Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations

arXiv: https://arxiv.org/abs/2010.03550 · HTML: https://mlsanity.com/paper/2010.03550/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 14 of 14)

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- [Jointly Extracting Interventions, Outcomes, and Findings from RCT Reports with LLMs](https://mlsanity.com/paper/2305.03642/) (2023)
- [What Would it Take to get Biomedical QA Systems into Practice?](https://mlsanity.com/paper/2109.10415/) (2021)
- [Automated tabulation of clinical trial results: A joint entity and relation extraction approach with transformer-based language representations](https://mlsanity.com/paper/2112.05596/) (2021)
- [Entity Anchored ICD Coding](https://mlsanity.com/paper/2208.07444/) (2022)
- [BLIAM: Literature-based Data Synthesis for Synergistic Drug Combination Prediction](https://mlsanity.com/paper/2302.06860/) (2023)
- [CCS Explorer: Relevance Prediction, Extractive Summarization, and Named Entity Recognition from Clinical Cohort Studies](https://mlsanity.com/paper/2211.00201/) (2022)
- [ACR: A Benchmark for Automatic Cohort Retrieval](https://mlsanity.com/paper/2406.14780/) (2024)
- [DKE-Research at SemEval-2024 Task 2: Incorporating Data Augmentation with Generative Models and Biomedical Knowledge to Enhance Inference Robustness](https://mlsanity.com/paper/2404.09206/) (2024)
- [Cohort Retrieval using Dense Passage Retrieval](https://mlsanity.com/paper/2507.01049/) (2025)

## Abstract (arXiv)

The best evidence concerning comparative treatment effectiveness comes from clinical trials, the results of which are reported in unstructured articles. Medical experts must manually extract information from articles to inform decision-making, which is time-consuming and expensive. Here we consider the end-to-end task of both (a) extracting treatments and outcomes from full-text articles describing clinical trials (entity identification) and, (b) inferring the reported results for the former with respect to the latter (relation extraction). We introduce new data for this task, and evaluate models that have recently achieved state-of-the-art results on similar tasks in Natural Language Processing. We then propose a new method motivated by how trial results are typically presented that outperforms these purely data-driven baselines. Finally, we run a fielded evaluation of the model with a non-profit seeking to identify existing drugs that might be re-purposed for cancer, showing the potential utility of end-to-end evidence extraction systems.