CAREER:Real-Time Nonparametric Machine Learning for Healthcare with Guarantees

职业：有保障的医疗保健实时非参数机器学习

• 批准号: 2047981
• 负责人: George Chen
• 金额: $ 58.39万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047981&HistoricalAwards=false
• 关键词: CAREER; Real; Time; Nonparametric; Machine

This NSF CAREER project is on developing new real-time machine learning models for healthcare. A key emphasis is on predicting durations at the individual patient level such as, time until death, time until recovering from a disease, and hospital length of stay. The models developed will not only take advantage of recent advances in deep learning but will also come with rigorous accuracy guarantees that say when, why, and how well they work. These models are "nonparametric" in that they are guaranteed to work under very few assumptions on the underlying data. Moreover, these models will be used to assist medical transport and clinical exam room scheduling systems that rely on predictions of how long patients spend in different stages of their clinical visits. This research has the potential to make patient care more personalized and to improve the efficiency of hospitals' resource use. In conducting this research, much of the work is in not only bringing together machine learning and healthcare communities, but also providing these communities with educational resources (workshops, tutorials, and a new book) that will better shape the mathematical and statistical foundations of the relatively new field of study at the intersection of machine learning and healthcare. To grow the community at this intersection, the PI plans to teach a new course on machine learning for healthcare and continue mentoring students at both undergraduate and graduate levels. Lessons learned will inform the PI's outreach efforts at the high school and college levels that bring computer science and probability concepts to a diverse audience.Despite numerous machine learning methodological advances in recent years, few machine learning models are deployed in clinical settings. The few that are, tend to be many-decades-old or capitalize on deep learning success stories in imaging applications. However, many healthcare prediction tasks remain challenging, where state-of-the-art models (deep-learning-based or not) struggle to produce accurate predictions. For example, many survival analysis problems (predicting time-to-event outcomes such as time until death, time until hospital discharge, etc) are hard. To complicate matters, in many such problems, collecting data for training or validation on real patients could be costly and require long-term studies (e.g., for predicting time until death for cancer patients, these durations could be on the time scale of years). To help practitioners decide on which models to use, reliability assurances in the form of statistical guarantees on prediction models would be extremely valuable. Moreover, for these models to assist in time-sensitive high-stakes decisions, they must scale to real-time clinical data streams. This project aims to develop a family of real-time machine learning models for healthcare that not only take advantage of state-of-the-art machine learning advances in deep neural networks and tree ensembles, but also come with rigorous accuracy and uncertainty guarantees. These guarantees hold even though the models do not make parametric assumptions on the distribution of the data. Moreover, the models developed will be tested in clinical settings for scheduling hospital resource use with the ultimate goal of deployment in hospital systems. A heavy emphasis is on survival analysis problems, which are extremely common in healthcare but less well-known within the machine learning community. The proposed research tightly integrates with an education plan of teaching key concepts at the intersection of machine learning and healthcare. This includes making statistical guarantees of machine learning algorithms more accessible and usable by practitioners through workshops and tutorials, introducing survival analysis to a general machine learning audience with a new book, and teaching students at the undergraduate and graduate level fundamental concepts of machine learning for healthcare by developing a new course.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.

这个NSF CAREER项目旨在为医疗保健开发新的实时机器学习模型。一个关键的重点是预测持续时间在个别病人的水平，如时间，直到死亡，时间，直到从疾病中恢复，和住院时间。开发的模型不仅将利用深度学习的最新进展，而且还将提供严格的准确性保证，说明何时，为什么以及如何工作。这些模型是“非参数”的，因为它们保证在对基础数据的很少假设下工作。此外，这些模型将用于辅助医疗运输和临床检查室调度系统，这些系统依赖于对患者在临床就诊的不同阶段花费多长时间的预测。这项研究有可能使病人的护理更加个性化，并提高医院的资源利用效率。在进行这项研究时，大部分工作不仅将机器学习和医疗保健社区结合在一起，而且还为这些社区提供教育资源（研讨会，教程和新书），这些资源将更好地塑造相对较新的研究领域的数学和统计基础。为了在这个交叉点发展社区，PI计划教授一门关于医疗保健机器学习的新课程，并继续指导本科和研究生阶段的学生。PI在高中和大学阶段的推广工作将借鉴这些经验教训，将计算机科学和概率概念带给不同的受众。尽管近年来机器学习方法取得了许多进展，但很少有机器学习模型被部署在临床环境中。少数几个是，往往是几十年的历史或利用深度学习在成像应用中的成功案例。然而，许多医疗保健预测任务仍然具有挑战性，最先进的模型（基于深度学习或不基于深度学习）难以产生准确的预测。例如，许多生存分析问题（预测事件发生时间，如死亡时间、出院时间等）是很难的。使问题复杂化的是，在许多这样的问题中，收集用于对真实的患者进行训练或验证的数据可能是昂贵的，并且需要长期的研究（例如，为了预测癌症患者死亡的时间，这些持续时间可以是年的时间尺度）。为了帮助从业者决定使用哪种模型，以统计保证的形式对预测模型进行可靠性保证将是极其有价值的。此外，为了帮助这些模型做出对时间敏感的高风险决策，它们必须扩展到实时临床数据流。该项目旨在为医疗保健开发一系列实时机器学习模型，不仅利用深度神经网络和树集成中最先进的机器学习技术，而且还具有严格的准确性和不确定性保证。即使模型没有对数据的分布做出参数假设，这些保证也是有效的。此外，开发的模型将在临床环境中进行测试，以调度医院资源的使用，最终目标是部署在医院系统中。重点是生存分析问题，这在医疗保健中非常常见，但在机器学习社区中不太知名。拟议的研究与机器学习和医疗保健交叉点的关键概念教学教育计划紧密结合。这包括通过研讨会和教程使从业者更容易获得和使用机器学习算法的统计保证，通过新书向一般机器学习受众介绍生存分析，并通过开发新课程向本科生和研究生教授医疗保健机器学习的基本概念。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。

项目成果

Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographics

分布稳健的生存分析：一种没有人口统计特征的新颖的公平损失

• 发表时间: 2023
• 期刊: Proceedings of the 2nd Machine Learning for Health symposium
• 作者: Hu, Shu;Chen, George H.
• 通讯作者: Chen, George H.

A General Framework for Visualizing Embedding Spaces of\titlebreak Neural Survival Analysis Models Based on Angular Information

基于角度信息的神经生存分析模型嵌入空间可视化通用框架

• 发表时间: 2023
• 期刊: and Learning
• 作者: Chen, George H.