SCH: Interpretable survival analysis of complex longitudinal data

SCH：复杂纵向数据的可解释生存分析

• 批准号: 2306556
• 负责人: Michele Santacatterina
• 金额: $ 116.84万
• 依托单位: New York University Medical Center
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2306556&HistoricalAwards=false
• 关键词: SCH; Interpretable; survival; analysis; complex

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.

生存分析是一种统计技术，用于预测特定事件发生之前的时间，例如住院、机械零件故障或客户流失。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不同来源的复杂纵向数据，包括不定期收集的文本、图像和实验室值，以预测患者的结果。传统的生存分析方法很难处理这些数据。该项目旨在开发新颖的深度学习技术、受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。该项目以之前成功利用 X 射线和实验室值来预测插管、死亡和 ICU 入院/出院等事件的工作为基础，将针对关键的临床应用对新方法进行基准测试。这项跨学科提案汇集了计算机科学、生物统计学和心脏病学领域的研究人员，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计划的支持。调查人员将与现有的康奈尔科技推广计划合作，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并促进 STEM 教育的包容性。通过生存分析解决大规模的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很成熟，但它们本质上并不能提供从长期、不规则和多模式输入中有效学习的解决方案，特别是在需要可解释性时。该项目的核心概念围绕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。该项目重点关注该领域至关重要的四个关键目标：（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，(4) 将这种特征表示与更先进的生存分析方法相结合。这些方法的评估将主要利用公开的 MIMIC 数据集，其中包括重症监护患者记录。此外，该项目团队与从事心力衰竭研究的临床医生合作，为应用评估提供了额外的数据集。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。