事件发生时间预测是生物发现的关键任务,实验医学,和临床护理。对于神经系统疾病尤其如此,在神经系统疾病中,可靠的生物标志物的开发通常受到可视化和采样相关细胞和分子病理学的困难的限制。迄今为止,由于易于使用,许多工作都依赖于Cox回归,尽管有证据表明这个模型包括不正确的假设。我们已经在完全可定制的“应用程序”和随附的在线门户中实现了一组用于时间到事件建模的深度学习和样条模型,这两种方法都可用于非专家用户对任何疾病的任何时间到事件分析。我们的在线门户为包括患者在内的最终用户提供了容量,神经内科临床医生,和研究人员,使用经过训练的模型访问和执行预测,并为模型改进提供新数据,所有这些都在数据安全的环境中。我们展示了一个使用我们的应用程序的管道,包括三个用例,包括缺失数据的填补,超参数调整,模型训练和独立验证。我们表明,预测最适合用于下游应用,如基因发现,生物标志物解释,和个性化的药物选择。我们展示了集成配置的效率,包括深度学习模型的集中培训。我们已经结合时间到事件预测模型优化了用于填补缺失数据的管道。总的来说,我们提供了一个强大且可访问的工具来开发,访问和共享时间到事件预测模型;所有软件和教程均可在www上获得。predictte.org。
Time-to-event prediction is a key task for biological discovery, experimental medicine, and clinical care. This is particularly true for neurological diseases where development of reliable biomarkers is often limited by difficulty visualising and sampling relevant cell and molecular pathobiology. To date, much work has relied on Cox regression because of ease-of-use, despite evidence that this model includes incorrect assumptions. We have implemented a set of deep learning and spline models for time-to-event modelling within a fully customizable \'app\' and accompanying online portal, both of which can be used for any time-to-event analysis in any disease by a non-expert user. Our online portal includes capacity for end-users including patients, Neurology clinicians, and researchers, to access and perform predictions using a trained model, and to contribute new data for model improvement, all within a data-secure environment. We demonstrate a pipeline for use of our app with three use-cases including imputation of missing data, hyperparameter tuning, model training and independent validation. We show that predictions are optimal for use in downstream applications such as genetic discovery, biomarker interpretation, and personalised choice of medication. We demonstrate the efficiency of an ensemble configuration, including focused training of a deep learning model. We have optimised a pipeline for imputation of missing data in combination with time-to-event prediction models. Overall, we provide a powerful and accessible tool to develop, access and share time-to-event prediction models; all software and tutorials are available at www.predictte.org.