# Language Interaction Network for Clinical Trial Approval Estimation

arXiv: https://arxiv.org/abs/2405.06662 · HTML: https://mlsanity.com/paper/2405.06662/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 26 Apr 2024.

## Builds on (most cited first, 12 of 12)

- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [GRAM: Graph-based Attention Model for Healthcare Representation Learning](https://mlsanity.com/paper/1611.07012/) (2016)
- [TransTab: Learning Transferable Tabular Transformers Across Tables](https://mlsanity.com/paper/2205.09328/) (2022)
- [Differentiable Scaffolding Tree for Molecular Optimization](https://mlsanity.com/paper/2109.10469/) (2021)
- [HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data](https://mlsanity.com/paper/2102.04252/) (2021)
- [COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching](https://mlsanity.com/paper/2006.08765/) (2020)
- [CORE: Automatic Molecule Optimization Using Copy & Refine Strategy](https://mlsanity.com/paper/1912.05910/) (2019)
- [TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model](https://mlsanity.com/paper/2404.01273/) (2024)
- [Uncertainty Quantification on Clinical Trial Outcome Prediction](https://mlsanity.com/paper/2401.03482/) (2024)
- [Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment](https://mlsanity.com/paper/1911.10395/) (2019)
- [Classifying Unstructured Clinical Notes via Automatic Weak Supervision](https://mlsanity.com/paper/2206.12088/) (2022)
- [GenoCraft: A Comprehensive, User-Friendly Web-Based Platform for High-Throughput Omics Data Analysis and Visualization](https://mlsanity.com/paper/2312.14249/) (2023)

## Built on by (most cited first, 1 of 1)

- [TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Models](https://mlsanity.com/paper/2407.13115/) (2024)

## Abstract (arXiv)

Clinical trial outcome prediction seeks to estimate the likelihood that a clinical trial will successfully reach its intended endpoint. This process predominantly involves the development of machine learning models that utilize a variety of data sources such as descriptions of the clinical trials, characteristics of the drug molecules, and specific disease conditions being targeted. Accurate predictions of trial outcomes are crucial for optimizing trial planning and prioritizing investments in a drug portfolio. While previous research has largely concentrated on small-molecule drugs, there is a growing need to focus on biologics-a rapidly expanding category of therapeutic agents that often lack the well-defined molecular properties associated with traditional drugs. Additionally, applying conventional methods like graph neural networks to biologics data proves challenging due to their complex nature. To address these challenges, we introduce the Language Interaction Network (LINT), a novel approach that predicts trial outcomes using only the free-text descriptions of the trials. We have rigorously tested the effectiveness of LINT across three phases of clinical trials, where it achieved ROC-AUC scores of 0.770, 0.740, and 0.748 for phases I, II, and III, respectively, specifically concerning trials involving biologic interventions.