HINT：利用Web数据预测试验结果的分层交互网络
HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data
摘要：
临床试验对药物开发至关重要，但耗时，昂贵，而且往往给患者带来负担。更重要的是，由于疗效、安全性或患者招募问题，临床试验面临不确定的结果。如果我们能更好地预测临床试验的结果，我们就可以避免进行那些不可避免地会失败的试验，更多的资源可以投入到有可能成功的试验中。在本文中，我们提出了层次交互作用网络（HINT），用于更一般的，所有疾病的临床试验结果预测，基于一个全面的和多样化的网络数据集，包括药物的分子信息，目标疾病信息，试验协议和生物医学知识。HINT首先将这些多模态数据编码为潜在嵌入，其中设计了一个填补模块来处理丢失的数据。接下来，这些嵌入将被输入到知识嵌入模块中，以生成知识嵌入，这些知识嵌入使用来自网络的关于药效学特性和试验风险的外部知识进行预训练。然后交互图模块将通过领域知识将所有嵌入连接起来，以充分捕获各种试验组件及其复杂关系以及它们对试验结果的影响。最后，HINT学习一个动态注意图神经网络来预测审判结果。综合实验结果表明，HINT具有较强的预测性能，I期、II期、III期和适应症结局预测的PR-AUC分别为0.772、0.607、0.623、0.703。它在PR-AUC方面也始终优于最佳基线方法，最高可达12.4%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
