ArticleScientific reports2021
Predictive modeling of clinical trial terminations using feature engineering and embedding learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- From Prompt to Drug: Toward Pharmaceutical Superintelligence.ACS central science · 2026Review
- Success rates of American clinical oncology trials by geographic factors.Scientific reports · 2026Article
- Approaches in analyzing predictors of trial failure: a scoping review and meta-epidemiological study.BMC medical research methodology · 2026Article
- Prediction of drug approvals in oncology using explainable artificial intelligence.Scientific reports · 2025Article
- Characteristics and potential predictors of prematurely terminated interventional clinical trials for ovarian cancer in the ClinicalTrials.gov database.BMC cancer · 2025Article
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- Predicting accrual success for better clinical trial resource allocation.Scientific reports · 2025Article
- A Novel Model Using ML Techniques for Clinical Trial Design and Expedited Patient Onboarding Process.ClinicoEconomics and outcomes research : CEOR · 2025Article
- The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review.AAPS PharmSciTech · 2024Review
- A clinical trial termination prediction model based on denoising autoencoder and deep survival regression.Quantitative biology (Beijing, China) · 2024Article
- A scoping review of machine learning for sepsis prediction- feature engineering strategies and model performance: a step towards explainability.Critical care (London, England) · 2024Article
- Selective Publication within Vascular Surgery: Characteristics of Discontinued and Unpublished Randomized Clinical Trials.Annals of vascular surgery · 2023Article
- New drugs and stock market: a machine learning framework for predicting pharma market reaction to clinical trial announcements.Scientific reports · 2023Article
- Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design.Pharmaceutics · 2023Review
- Deep learning-based risk prediction for interventional clinical trials based on protocol design: A retrospective study.Patterns (New York, N.Y.) · 2023Article
- Early Termination of Oncology Clinical Trials in the United States.Cancer medicine · 2023Article
- The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives.Drug design, development and therapy · 2023Article
- Predicting Publication of Clinical Trials Using Structured and Unstructured Data: Model Development and Validation Study.Journal of medical Internet research · 2022Article
- A knowledge graph of clinical trials ([Formula: see text]).Scientific reports · 2022Article
- Predicting completion of clinical trials in pregnant women: Cox proportional hazard and neural network models.Clinical and translational science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we propose to use machine learning to understand terminated clinical trials. Our goal is to answer two fundamental questions: (1) what are common factors/markers associated to terminated clinical trials? and (2) how to accurately predict whether a clinical trial may be terminated or not? The answer to the first question provides effective ways to understand characteristics of terminated trials for stakeholders to better plan their trials; and the answer to the second question can direct estimate the chance of success of a clinical trial in order to minimize costs. By using 311,260 trials to build a testbed with 68,999 samples, we use feature engineering to create 640 features, reflecting clinical trial administration, eligibility, study information, criteria etc. Using feature ranking, a handful of features, such as trial eligibility, trial inclusion/exclusion criteria, sponsor types etc., are found to be related to the clinical trial termination. By using sampling and ensemble learning, we achieve over 67% Balanced Accuracy and over 0.73 AUC (Area Under the Curve) scores to correctly predict clinical trial termination, indicating that machine learning can help achieve satisfactory prediction results for clinical trial study.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.