# Uncertainty Quantification on Clinical Trial Outcome Prediction

arXiv: https://arxiv.org/abs/2401.03482 · HTML: https://mlsanity.com/paper/2401.03482/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 64 references that are in the map.
- First posted on arXiv 7 Jan 2024.

## Builds on (most cited first, 20 of 20)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Selective Classification for Deep Neural Networks](https://mlsanity.com/paper/1705.08500/) (2017)
- [SelectiveNet: A Deep Neural Network with an Integrated Reject Option](https://mlsanity.com/paper/1901.09192/) (2019)
- [Consistent Estimators for Learning to Defer to an Expert](https://mlsanity.com/paper/2006.01862/) (2020)
- [Training Very Deep Networks](https://mlsanity.com/paper/1507.06228/) (2015)
- [GRAM: Graph-based Attention Model for Healthcare Representation Learning](https://mlsanity.com/paper/1611.07012/) (2016)
- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [Direct Uncertainty Prediction for Medical Second Opinions](https://mlsanity.com/paper/1807.01771/) (2018)
- [Differentiable Scaffolding Tree for Molecular Optimization](https://mlsanity.com/paper/2109.10469/) (2021)
- [MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization](https://mlsanity.com/paper/2010.02318/) (2020)
- [HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data](https://mlsanity.com/paper/2102.04252/) (2021)
- [Reinforced Genetic Algorithm for Structure-based Drug Design](https://mlsanity.com/paper/2211.16508/) (2022)
- [Regression Under Human Assistance](https://mlsanity.com/paper/1909.02963/) (2019)
- [CORE: Automatic Molecule Optimization Using Copy & Refine Strategy](https://mlsanity.com/paper/1912.05910/) (2019)
- [Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial](https://mlsanity.com/paper/2305.04933/) (2023)
- [Unanimous Prediction for 100% Precision with Application to Learning Semantic Mappings](https://mlsanity.com/paper/1606.06368/) (2016)
- [Artificial Intelligence for In Silico Clinical Trials: A Review](https://mlsanity.com/paper/2209.09023/) (2022)

## Built on by (most cited first, 19 of 19)

- [DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration](https://mlsanity.com/paper/2411.15692/) (2024)
- [ClinicalAgent: Clinical Trial Multi-Agent System with Large Language Model-based Reasoning](https://mlsanity.com/paper/2404.14777/) (2024)
- [TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model](https://mlsanity.com/paper/2404.01273/) (2024)
- [SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction](https://mlsanity.com/paper/2408.05696/) (2024)
- [Multimodal Clinical Trial Outcome Prediction with Large Language Models](https://mlsanity.com/paper/2402.06512/) (2024)
- [TrialDura: Hierarchical Attention Transformer for Interpretable Clinical Trial Duration Prediction](https://mlsanity.com/paper/2404.13235/) (2024)
- [Structure-based Drug Design Benchmark: Do 3D Methods Really Dominate?](https://mlsanity.com/paper/2406.03403/) (2024)
- [DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing](https://mlsanity.com/paper/2407.02265/) (2024)
- [TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets](https://mlsanity.com/paper/2407.00631/) (2024)
- [TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Models](https://mlsanity.com/paper/2407.13115/) (2024)
- [Protein-Mamba: Biological Mamba Models for Protein Function Prediction](https://mlsanity.com/paper/2409.14617/) (2024)
- [Gradient GA: Gradient Genetic Algorithm for Drug Molecular Design](https://mlsanity.com/paper/2502.09860/) (2025)
- [Language Interaction Network for Clinical Trial Approval Estimation](https://mlsanity.com/paper/2405.06662/) (2024)
- [DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning](https://mlsanity.com/paper/2410.02023/) (2024)
- [ClinicalReTrial: Clinical Trial Redesign with Self-Evolving Agents](https://mlsanity.com/paper/2601.00290/) (2026)
- [How to Train Text Summarization Model with Weak Supervisions](https://mlsanity.com/paper/2409.00098/) (2024)
- [TrialSynth: Generation of Synthetic Sequential Clinical Trial Data](https://mlsanity.com/paper/2409.07089/) (2024)
- [Quantum-inspired Reinforcement Learning for Synthesizable Drug Design](https://mlsanity.com/paper/2409.09183/) (2024)
- [Bladder Cancer Diagnosis with Deep Learning: A Multi-Task Framework and Online Platform](https://mlsanity.com/paper/2508.15379/) (2025)

## Abstract (arXiv)

The importance of uncertainty quantification is increasingly recognized in the diverse field of machine learning. Accurately assessing model prediction uncertainty can help provide deeper understanding and confidence for researchers and practitioners. This is especially critical in medical diagnosis and drug discovery areas, where reliable predictions directly impact research quality and patient health. In this paper, we proposed incorporating uncertainty quantification into clinical trial outcome predictions. Our main goal is to enhance the model's ability to discern nuanced differences, thereby significantly improving its overall performance. We have adopted a selective classification approach to fulfill our objective, integrating it seamlessly with the Hierarchical Interaction Network (HINT), which is at the forefront of clinical trial prediction modeling. Selective classification, encompassing a spectrum of methods for uncertainty quantification, empowers the model to withhold decision-making in the face of samples marked by ambiguity or low confidence, thereby amplifying the accuracy of predictions for the instances it chooses to classify. A series of comprehensive experiments demonstrate that incorporating selective classification into clinical trial predictions markedly enhances the model's performance, as evidenced by significant upticks in pivotal metrics such as PR-AUC, F1, ROC-AUC, and overall accuracy. Specifically, the proposed method achieved 32.37%, 21.43%, and 13.27% relative improvement on PR-AUC over the base model (HINT) in phase I, II, and III trial outcome prediction, respectively. When predicting phase III, our method reaches 0.9022 PR-AUC scores. These findings illustrate the robustness and prospective utility of this strategy within the area of clinical trial predictions, potentially setting a new benchmark in the field.