SCH: Interpretable survival analysis of complex longitudinal data

生存分析是一种统计技术，用于预测特定事件发生的时间，如住院治疗、机械部件故障或客户流失。它在医疗保健领域的应用横跨公共卫生、临床实践和医学研究。临床医生面临的挑战是整合来自不同来源的复杂纵向数据，包括不规则间隔收集的文本、图像和实验室值，以预测患者的预后。传统的生存分析方法很难处理这样的数据。该项目旨在开发新颖的深度学习技术，以大脑为灵感的计算机模型来分析复杂的数据，为此目的量身定制。重要的是，这些技术将提供特定于医疗保健领域的可解释性，增强用户对预测的信心。在先前成功利用x射线和实验室值预测插管、死亡和ICU入院/出院等事件的基础上，该项目将对关键临床应用的新方法进行基准测试。这项跨学科的提案汇集了计算机科学、生物统计学和心脏病学方面的研究人员，以显著增强关键医疗保健背景下的生存分析模型。该研究将产生新的预测方法和模型解释，并在探索医疗保健挑战的开放数据集上进行演示。此外，该提案还包括对以生存分析为中心的教育推广项目的支持。调查人员将与康奈尔大学现有的科技推广计划合作，通过与纽约市立大学（CUNY）和纽约市教育部的合作，针对女性和代表性不足的少数族裔。通过结合专业知识，该项目旨在推动生存分析的创新，促进STEM教育的包容性。通过生存分析解决大规模现实世界医疗保健挑战需要解决与数据表示和建模相关的复杂问题。虽然像Cox模型这样的经典生存分析方法已经得到了完善，但它们本身并不能为从长期、不规则和多模态输入中有效学习提供解决方案，特别是在需要可解释性的情况下。该项目的核心概念围绕一个统一的深度学习模型进行生存分析，该模型使用Transformer主干构建，旨在处理复杂的纵向数据。该项目重点关注该领域的四个关键目标：(1)为多模态数据提供统一的特征表示；(2)处理长期不规则间隔输入；(3)通过与领域专家合作，利用他们的专业知识作为软先验，支持定制的模型可解释性；(4)将这种特征表示与更先进的生存分析方法集成在一起。这些方法的评估将主要利用公开可用的MIMIC数据集，包括重症监护患者记录。此外，项目团队与心力衰竭临床医生合作，为应用评估提供额外的数据集。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Survival analysis is a statistical technique used to predict the time until specific events occur, such as hospitalization, mechanical part failure, or customer churn. Its applications in healthcare span across public health, clinical practice, and medical research. Clinicians face the challenge of integrating complex longitudinal data from various sources, including text, images, and lab values, collected at irregular intervals, to predict patient outcomes. Traditional survival analysis methods struggle with such data. This project aims to develop novel deep learning techniques, brain-inspired computer models to analyze complex data, tailored for this purpose. Importantly, these techniques will offer interpretability specific to the healthcare domain, bolstering users' confidence in the predictions. Building upon prior work that successfully utilized X-rays and lab values to predict events like intubation, death, and ICU admission/discharge, this project will benchmark the new methods against crucial clinical applications. This interdisciplinary proposal brings together researchers specializing in computer science, biostatistics and cardiology to significantly enhance models for survival analysis in a crucial healthcare context. The research will yield new prediction methods and model interpretations, demonstrated on open datasets that explore healthcare challenges. Moreover, the proposal includes support for educational outreach programs centered around survival analysis. The investigators will collaborate with existing Cornell Tech outreach initiatives, targeting women and underrepresented minorities through partnerships with the City University of New York (CUNY) and the New York City Department of Education. By combining expertise, this project aims to drive innovation in survival analysis and promote inclusivity in STEM education.Addressing large-scale real-world healthcare challenges with survival analysis necessitates solving complex issues related to data representation and modeling. While classical survival analysis methods like the Cox model are well-established, they do not inherently provide solutions for effective learning from long-term, irregular, and multi-modal inputs, particularly when interpretability is required. The core concept of this project revolves around a unified deep learning model for survival analysis, constructed using a Transformer backbone designed to handle complex longitudinal data. The project focuses on four key aims essential to this domain: (1) providing a unified feature representation for multi-modal data, (2) handling long-term irregularly spaced input, (3) supporting customized model interpretability through collaboration with domain experts, leveraging their expertise as soft priors, and (4) integrating this feature representation with more advanced survival analysis methodologies. The evaluation of these methods will primarily utilize the publicly available MIMIC dataset, consisting of critical care patient records. Additionally, the project team collaborates with clinicians working on heart failure, providing an additional dataset for applied evaluation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.