HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data

• URL: http://arxiv.org/abs/2102.04252v1
• Date: Mon, 8 Feb 2021 15:09:07 GMT
• Title: HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data
• Authors: Tianfan Fu, Kexin Huang, Cao Xiao, Lucas M. Glass, Jimeng Sun
• Abstract summary: Clinical trials face uncertain outcomes due to issues with efficacy, safety, or problems with patient recruitment. In this paper, we propose Hierarchical INteraction Network (HINT) for more general, clinical trial outcome predictions.
• Score: 56.53715632642495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clinical trials are crucial for drug development but are time consuming, expensive, and often burdensome on patients. More importantly, clinical trials face uncertain outcomes due to issues with efficacy, safety, or problems with patient recruitment. If we were better at predicting the results of clinical trials, we could avoid having to run trials that will inevitably fail more resources could be devoted to trials that are likely to succeed. In this paper, we propose Hierarchical INteraction Network (HINT) for more general, clinical trial outcome predictions for all diseases based on a comprehensive and diverse set of web data including molecule information of the drugs, target disease information, trial protocol and biomedical knowledge. HINT first encode these multi-modal data into latent embeddings, where an imputation module is designed to handle missing data. Next, these embeddings will be fed into the knowledge embedding module to generate knowledge embeddings that are pretrained using external knowledge on pharmaco-kinetic properties and trial risk from the web. Then the interaction graph module will connect all the embedding via domain knowledge to fully capture various trial components and their complex relations as well as their influences on trial outcomes. Finally, HINT learns a dynamic attentive graph neural network to predict trial outcome. Comprehensive experimental results show that HINT achieves strong predictive performance, obtaining 0.772, 0.607, 0.623, 0.703 on PR-AUC for Phase I, II, III, and indication outcome prediction, respectively. It also consistently outperforms the best baseline method by up to 12.4\% on PR-AUC.

Related papers

• TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [57.067409211231244]
  This paper presents meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design. We provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
  arXiv  Detail & Related papers  (2024-06-30T09:13:10Z)
• Automatically Labeling $200B Life-Saving Datasets: A Large Clinical Trial Outcome Benchmark [24.663798850232588]
  This paper introduces Clinical Trial Outcome (CTO) dataset, the largest trial outcome dataset with around 479K clinical trials. CTO's labels show unprecedented agreement with supervised clinical trial outcome labels from test split of the supervised TOP dataset, with a 91 F1.
  arXiv  Detail & Related papers  (2024-06-13T04:23:35Z)
• Language Interaction Network for Clinical Trial Approval Estimation [37.60098683485169]
  We introduce the Language Interaction Network (LINT), a novel approach that predicts trial outcomes using only the free-text descriptions of the trials. We have rigorously tested LINT across three phases of clinical trials, where it achieved ROC-AUC scores of 0.770, 0.740, and 0.748.
  arXiv  Detail & Related papers  (2024-04-26T14:50:59Z)
• CT-ADE: An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results [0.10051474951635876]
  Adverse drug events (ADEs) significantly impact clinical research, causing many clinical trial failures. To support this effort, we introduce CT-ADE, a dataset for multilabel predictive modeling of ADEs in monopharmacy treatments. CT-ADE integrates data from 2,497 unique drugs, encompassing 168,984 drug-ADE pairs extracted from clinical trials, annotated with patient and contextual information, and comprehensive ADE concepts standardized across multiple levels of the MedDRA.
  arXiv  Detail & Related papers  (2024-04-19T12:04:32Z)
• Physical formula enhanced multi-task learning for pharmacokinetics prediction [54.13787789006417]
  A major challenge for AI-driven drug discovery is the scarcity of high-quality data. We develop a formula enhanced mul-ti-task learning (PEMAL) method that predicts four key parameters of pharmacokinetics simultaneously. Our experiments reveal that PEMAL significantly lowers the data demand, compared to typical Graph Neural Networks.
  arXiv  Detail & Related papers  (2024-04-16T07:42:55Z)
• TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic Tree-Based Memory Network [54.332862955411656]
  Clinical trials are critical for drug development but often suffer from expensive and inefficient patient recruitment. In recent years, machine learning models have been proposed for speeding up patient recruitment via automatically matching patients with clinical trials. We introduce a dynamic tree-based memory network model named TREEMENT to provide accurate and interpretable patient trial matching.
  arXiv  Detail & Related papers  (2023-07-19T12:35:09Z)
• SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning [67.8195828626489]
  Clinical trials are essential to drug development but time-consuming, costly, and prone to failure. 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. 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.
  arXiv  Detail & Related papers  (2023-04-07T23:04:27Z)
• Modular multi-source prediction of drug side-effects with DruGNN [3.229607826010618]
  Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. To predict their occurrence, it is necessary to integrate data from heterogeneous sources. In this work, such heterogeneous data is integrated into a graph dataset, expressively representing the relational information between different entities. Graph Neural Networks (GNNs) are exploited to predict DSEs on our dataset with very promising results.
  arXiv  Detail & Related papers  (2022-02-15T09:41:05Z)
• DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations [90.27736364704108]
  We present DrugOOD, a systematic OOD dataset curator and benchmark for AI-aided drug discovery. DrugOOD comes with an open-source Python package that fully automates benchmarking processes. We focus on one of the most crucial problems in AIDD: drug target binding affinity prediction.
  arXiv  Detail & Related papers  (2022-01-24T12:32:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.