mlsanityData to 7 Oct 2026

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

arXiv 2304.05352 ↗First posted 7 Apr 2023cs.LG cs.AIcited by 13

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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).

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

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17 of 33 references.

References · 17

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5 more
  1. 2018Meta-Reinforcement Learning of Structured Exploration Strategiescited by 128
  2. 2021HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Datacited by 49
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 3; 2025: 4; 2026: 3.

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  1. 2023MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinementcited by 24
  2. 2024Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trialscited by 24
  3. 2023A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promisescited by 13
  4. 2025AUTOCT: Automating Interpretable Clinical Trial Prediction with LLM Agentscited by 9
  5. 2024Multimodal Clinical Trial Outcome Prediction with Large Language Modelscited by 7
  6. 2024Automatically Labeling Clinical Trial Outcomes: A Large-Scale Benchmark for Drug Developmentcited by 7
  7. 2023PyTrial: Machine Learning Software and Benchmark for Clinical Trial Applicationscited by 3
  8. 2026CT Open: An Open-Access, Uncontaminated, Live Platform for the Open Challenge of Clinical Trial Outcome Predictioncited by 2
  9. 2025PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation modelscited by 1
  10. 2025Developing Large Language Models for Clinical Research Using One Million Clinical Trialscited by 1
  11. 2025Deep Learning-based Prediction of Clinical Trial Enrollment with Uncertainty Estimatescited by 0
  12. 2026DeepImagine: Clinical Trial Outcome Prediction via Stepwise Local Counterfactual Imaginationscited by 0
1 more
  1. 2026TrialAtlas: Multi-Agent Research Organization for Clinical Trial Design and Optimizationcited by 0