ArticlePatterns (New York, N.Y.)2022
HINT: Hierarchical interaction network for clinical-trial-outcome predictions.
Article in Patterns (New York, N.Y.), 2022. 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, 49 citations in OpenAlex.
- AI-based augmentation of oncology clinical trials.Nature reviews. Clinical oncology · 2026Review
- Big Data and Artificial Intelligence in Cancer Drug Discovery: Promise, Challenges, and Emerging Opportunities.Cancers · 2026Review
- A genomic-led strategy to anticipate drug safety effects.PLoS genetics · 2026Article
- drGT: interpretable drug response prediction with attention-guided gene attribution on a drug-cell-gene heterogeneous graph.BMC bioinformatics · 2026Article
- From Prompt to Drug: Toward Pharmaceutical Superintelligence.ACS central science · 2026Review
- Artificial intelligence for clinical trial design, conduct, and analysis: a narrative review.ESMO real world data and digital oncology · 2026Review
- The Virtual Biotech: A Multi-Agent AI Framework for Therapeutic Discovery and Development.bioRxiv : the preprint server for biology · 2026Article
- Success rates of American clinical oncology trials by geographic factors.Scientific reports · 2026Article
- Rethinking research in urology: pitfalls from the Talent Incubator Programme.BJU international · 2025Article
- Large language models for drug discovery and development.Patterns (New York, N.Y.) · 2025Review
- TrialBench: Multi-Modal AI-Ready Datasets for Clinical Trial Prediction.Scientific data · 2025Article
- A review on knowledge graphs for healthcare: Resources, applications, and promises.Journal of biomedical informatics · 2025Review
- drGT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network.ArXiv · 2025Article
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics.bioRxiv : the preprint server for biology · 2024Article
- Application of Virtual Drug Study to New Drug Research and Development: Challenges and Opportunity.Clinical pharmacokinetics · 2024Review
- Cross-Modal Graph Contrastive Learning with Cellular Images.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- FRAMM: Fair ranking with missing modalities for clinical trial site selection.Patterns (New York, N.Y.) · 2024Article
- Uncertainty Quantification and Interpretability for Clinical Trial Approval Prediction.Health data science · 2024Article
- ClinicalRisk: A New Therapy-related Clinical Trial Dataset for Predicting Trial Status and Failure Reasons.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
Clinical trials are crucial for drug development but often face uncertain outcomes due to safety, efficacy, or patient-recruitment problems. We propose the Hierarchical Interaction Network (HINT) to predict clinical trial outcomes. First, HINT encodes multi-modal data (drug molecule, target disease, trial eligibility criteria) into embeddings. Then, HINT trains knowledge-embedding modules using drug pharmacokinetic and historical trial data. Finally, a hierarchical interaction graph connects all of the embeddings to capture their interactions and predict trial outcomes. HINT was trained and validated on 1,160 phase I trials, 4,449 phase II trials, and 3,436 phase III trials. It obtained 0.665, 0.620, and 0.847 F1 scores on separate test sets of 627 phase I, 1,653 phase II, and 1,140 phase III trials, respectively. HINT significantly outperforms the best baseline method on most metrics. The benchmark dataset and codes are released at https://github.com/futianfan/clinical-trial-outcome-prediction.
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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.