# SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning

arXiv: https://arxiv.org/abs/2304.05352 · HTML: https://mlsanity.com/paper/2304.05352/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 33 references that are in the map.
- First posted on arXiv 7 Apr 2023.

## Builds on (most cited first, 17 of 17)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [On First-Order Meta-Learning Algorithms](https://mlsanity.com/paper/1803.02999/) (2018)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [RL2: Fast Reinforcement Learning via Slow Reinforcement Learning](https://mlsanity.com/paper/1611.02779/) (2016)
- [Dynamic Few-Shot Visual Learning without Forgetting](https://mlsanity.com/paper/1804.09458/) (2018)
- [Meta-SGD: Learning to Learn Quickly for Few-Shot Learning](https://mlsanity.com/paper/1707.09835/) (2017)
- [Highway Networks](https://mlsanity.com/paper/1505.00387/) (2015)
- [SMASH: One-Shot Model Architecture Search through HyperNetworks](https://mlsanity.com/paper/1708.05344/) (2017)
- [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)
- [Meta-Reinforcement Learning of Structured Exploration Strategies](https://mlsanity.com/paper/1802.07245/) (2018)
- [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)
- [Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision](https://mlsanity.com/paper/2206.14719/) (2022)
- [Artificial Intelligence for In Silico Clinical Trials: A Review](https://mlsanity.com/paper/2209.09023/) (2022)

## Built on by (most cited first, 13 of 13)

- [MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement](https://mlsanity.com/paper/2305.12081/) (2023)
- [Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials](https://mlsanity.com/paper/2409.04481/) (2024)
- [A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises](https://mlsanity.com/paper/2306.04802/) (2023)
- [AUTOCT: Automating Interpretable Clinical Trial Prediction with LLM Agents](https://mlsanity.com/paper/2506.04293/) (2025)
- [Multimodal Clinical Trial Outcome Prediction with Large Language Models](https://mlsanity.com/paper/2402.06512/) (2024)
- [Automatically Labeling Clinical Trial Outcomes: A Large-Scale Benchmark for Drug Development](https://mlsanity.com/paper/2406.10292/) (2024)
- [PyTrial: Machine Learning Software and Benchmark for Clinical Trial Applications](https://mlsanity.com/paper/2306.04018/) (2023)
- [CT Open: An Open-Access, Uncontaminated, Live Platform for the Open Challenge of Clinical Trial Outcome Prediction](https://mlsanity.com/paper/2604.16742/) (2026)
- [PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation models](https://mlsanity.com/paper/2505.05577/) (2025)
- [Developing Large Language Models for Clinical Research Using One Million Clinical Trials](https://mlsanity.com/paper/2505.16097/) (2025)
- [Deep Learning-based Prediction of Clinical Trial Enrollment with Uncertainty Estimates](https://mlsanity.com/paper/2507.23607/) (2025)
- [DeepImagine: Clinical Trial Outcome Prediction via Stepwise Local Counterfactual Imaginations](https://mlsanity.com/paper/2604.23054/) (2026)
- [TrialAtlas: Multi-Agent Research Organization for Clinical Trial Design and Optimization](https://mlsanity.com/paper/2609.21859/) (2026)

## Abstract (arXiv)

Clinical trials are essential to drug development but time-consuming, costly, and prone to failure. Accurate trial outcome prediction based on historical trial data promises better trial investment decisions and more trial success. Existing trial outcome prediction models were not designed to model the relations among similar trials, capture the progression of features and designs of similar trials, or address the skewness of trial data which causes inferior performance for less common trials. To fill the gap and provide accurate trial outcome prediction, we propose Sequential Predictive mOdeling of clinical Trial outcome (SPOT) that first identifies trial topics to cluster the multi-sourced trial data into relevant trial topics. It then generates trial embeddings and organizes them by topic and time to create clinical trial sequences. With the consideration of each trial sequence as a task, it uses a meta-learning strategy to achieve a point where the model can rapidly adapt to new tasks with minimal updates. In particular, the topic discovery module enables a deeper understanding of the underlying structure of the data, while sequential learning captures the evolution of trial designs and outcomes. This results in predictions that are not only more accurate but also more interpretable, taking into account the temporal patterns and unique characteristics of each trial topic. We demonstrate that SPOT wins over the prior methods by a significant margin on trial outcome benchmark data: with a 21.5% lift on phase I, an 8.9% lift on phase II, and a 5.5% lift on phase III trials in the metric of the area under precision-recall curve (PR-AUC).