# Predicting Clinical Trial Results by Implicit Evidence Integration

arXiv: https://arxiv.org/abs/2010.05639 · HTML: https://mlsanity.com/paper/2010.05639/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [Matching Patients to Clinical Trials with Large Language Models](https://mlsanity.com/paper/2307.15051/) (2023)
- [CTP-LLM: Clinical Trial Phase Transition Prediction Using Large Language Models](https://mlsanity.com/paper/2408.10995/) (2024)
- [MEXA-CTP: Mode Experts Cross-Attention for Clinical Trial Outcome Prediction](https://mlsanity.com/paper/2501.06823/) (2025)
- [Developing Large Language Models for Clinical Research Using One Million Clinical Trials](https://mlsanity.com/paper/2505.16097/) (2025)
- [Predicting Intervention Approval in Clinical Trials through Multi-Document Summarization](https://mlsanity.com/paper/2204.00290/) (2022)
- [CLaDMoP: Learning Transferrable Models from Successful Clinical Trials via LLMs](https://mlsanity.com/paper/2505.18527/) (2025)

## Abstract (arXiv)

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.