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，例如住院，机械部件故障或客户流失。其在医疗保健领域的应用涵盖公共卫生、临床实践和医学研究。临床医生面临的挑战是整合来自各种来源的复杂纵向数据，包括文本，图像和实验室值，以不规则的时间间隔收集，以预测患者的结果。传统的生存分析方法难以处理这些数据。该项目旨在开发新颖的深度学习技术，大脑启发的计算机模型来分析复杂的数据，并为此目的量身定制。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/出院等事件的工作基础上，该项目将对新方法进行基准测试，以应对关键的临床应用。这项跨学科的提案汇集了专门从事计算机科学，生物统计学和心脏病学的研究人员，以显着增强关键医疗保健环境中的生存分析模型。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展示。此外，该提案还包括支持以生存分析为中心的教育推广计划。调查人员将与现有的康奈尔理工学院外展计划合作，通过与纽约（CUNY）和纽约市教育局的伙伴关系，针对妇女和代表性不足的少数民族。通过结合专业知识，该项目旨在推动生存分析的创新，并促进STEM教育的包容性。通过生存分析解决大规模现实世界的医疗挑战需要解决与数据表示和建模相关的复杂问题。虽然经典的生存分析方法，如考克斯模型是完善的，他们并不固有地提供有效的学习从长期的解决方案，不规则的，多模态的输入，特别是当需要解释。该项目的核心概念围绕着用于生存分析的统一深度学习模型，该模型使用旨在处理复杂纵向数据的Transformer主干构建。该项目侧重于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的输入，（3）通过与领域专家合作支持定制模型的可解释性，利用他们的专业知识作为软先验，以及（4）将此特征表示与更先进的生存分析方法相结合。这些方法的评价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，该项目团队还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NSF的法定使命，并被认为值得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来支持。

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.