QuBBD: From Personalized Predictions to Better Control of Chronic Health Conditions

QuBBD：从个性化预测到更好地控制慢性健康状况

基本信息

批准号：
1664644
负责人：
Ioannis Paschalidis
金额：
$ 90万
依托单位：
Trustees of Boston University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-01 至 2023-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1664644&HistoricalAwards=false
关键词：
QuBBD Personalized Predictions Better Control

项目摘要

The United States spends twice as much annually on health care than the next-highest spender but significantly under-performs in quality of care metrics, such as life expectancy and infant mortality. Hospital care accounts for about a third of U.S. health care spending. It has been estimated that nearly $30 billion in hospital care costs each year are potentially preventable, with about half of that amount due to hospitalizations related to the two major chronic diseases: heart diseases and diabetes. Electronic Health Records, and the emerging digital data from home-based devices, smart phones, and wearables, offer a great opportunity to develop a systematic approach towards better management of chronic conditions in an outpatient setting and the prevention of hospitalizations required to treat acute episodes resulting from poor control of a patient's condition. This project will utilize digital health data to develop predictive models that anticipate future undesirable events, such as hospitalizations, re-admissions, and transitioning to an acute stage of a disease. These predictions will be used to trigger personalized interventions, ranging from increased monitoring and doctor visits to optimized treatment policies adapted to each patient. The project supports a collaboration between mathematical scientists and a physician at a major safety-net hospital, which treats a significant percentage of low-income and underrepresented groups.The research will focus on two broad tasks: (1) predictive analytics, and (2) personalized interventions. Task 1 develops methods for predictions in two time scales, long and medium. These predictions target hospitalizations and rely upon new supervised machine learning approaches that combine classification with clustering as a way of enhancing performance and offering interpretable results. In addition, anomaly detection methods are proposed for shorter-term predictions. Task 2 focuses on interventions seeking to prevent events predicted under Task 1. Interventions include increased monitoring and optimizing treatment policies using Markov Decision Processes and perturbation analysis methods. Methodological advances will include methods for joint clustering and classification, anomaly detection, learning and improving policies for Markov Decision Processes, and perturbation analysis techniques.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.

美国每年在医疗保健上的支出是第二高支出国家的两倍，但在预期寿命和婴儿死亡率等医疗质量指标方面表现明显不佳。医院护理约占美国医疗保健支出的三分之一。据估计，每年有近 300 亿美元的医院护理费用是可以避免的，其中约一半是由于与心脏病和糖尿病这两种主要慢性疾病相关的住院治疗。电子健康记录以及来自家庭设备、智能手机和可穿戴设备的新兴数字数据，为开发系统方法提供了绝佳的机会，以更好地管理门诊慢性病并预防治疗急性发作所需的住院治疗是由于对患者病情控制不力造成的。该项目将利用数字健康数据开发预测模型，预测未来的不良事件，例如住院、重新入院和过渡到疾病的急性阶段。这些预测将用于触发个性化干预措施，包括加强监测和就诊，以及适合每位患者的优化治疗政策。该项目支持数学科学家和一家大型安全网医院的医生之间的合作，该医院为很大一部分低收入和代表性不足的群体提供治疗。该研究将重点关注两项广泛的任务：（1）预测分析，以及（2 ）个性化干预。任务 1 开发了两个时间尺度（长期和中期）的预测方法。这些预测以住院治疗为目标，并依赖于新的监督机器学习方法，将分类与聚类相结合，作为增强性能和提供可解释结果的一种方式。此外，还提出了用于短期预测的异常检测方法。任务 2 的重点是寻求预防任务 1 中预测的事件的干预措施。干预措施包括使用马尔可夫决策过程和扰动分析方法加强监测和优化治疗政策。方法论的进步将包括联合聚类和分类、异常检测、马尔可夫决策过程的学习和改进策略以及扰动分析技术的方法。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和更广泛的评估进行评估，被认为值得支持。影响审查标准。

项目成果

期刊论文数量（160）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Bridging the Gap between Optimal Trajectory Planning and Safety-Critical Control with Applications to Autonomous Vehicles

通过在自动驾驶汽车上的应用来缩小最佳轨迹规划和安全关键控制之间的差距

DOI：
10.1016/j.automatica.2021.109592
发表时间：
2021
期刊：
Automatica
影响因子：
6.4
作者：
Xiao, W.;Cassandras, C.G.;Belta, C.
通讯作者：
Belta, C.

Decentralized optimal merging control for Connected and Automated Vehicles with safety constraint guarantees

DOI：
10.1016/j.automatica.2020.109333
发表时间：
2021-01-01
期刊：
AUTOMATICA
影响因子：
6.4
作者：
Xiao, Wei;Cassandras, Christos G.
通讯作者：
Cassandras, Christos G.

Safe Merging Control in Mixed Vehicular Traffic

DOI：
10.23919/acc55779.2023.10156078
发表时间：
2023-05
期刊：
2023 American Control Conference (ACC)
影响因子：
0
作者：
Vahid Hamdipoor;N. Meskin;C. Cassandras
通讯作者：
Vahid Hamdipoor;N. Meskin;C. Cassandras

Improved prediction of MHC-peptide binding using protein language models.

DOI：
10.3389/fbinf.2023.1207380
发表时间：
2023
期刊：
FRONTIERS IN BIOINFORMATICS
影响因子：
0
作者：
Hashemi, Nasser;Hao, Boran;Ignatov, Mikhail;Paschalidis, Ioannis Ch;Vakili, Pirooz;Vajda, Sandor;Kozakov, Dima
通讯作者：
Kozakov, Dima

Distributionally Robust Multiclass Classification and Applications in Deep Image Classifiers

分布式鲁棒多类分类及其在深度图像分类器中的应用

DOI：
10.1109/icassp49357.2023.10095775
发表时间：
2023
期刊：
and Signal Processing (ICASSP
影响因子：
0
作者：
Chen, Ruidi;Hao, Boran;Paschalidis, Ioannis Ch.
通讯作者：
Paschalidis, Ioannis Ch.

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Ioannis Paschalidis其他文献

Sensor and Actuator Placement for Linear Systems Based on H2 and H∞ Optimization

基于 H2 和 H∞ 优化的线性系统的传感器和执行器放置

DOI：
10.1002/wcm.622
发表时间：
2014
期刊：
Wirel. Commun. Mob. Comput.
影响因子：
0
作者：
Francesco Bullo;P. Antsaklis;Thomas Parisini;Ioannis Paschalidis;R. D. Braatz;Maria Prandini;U. Münz;M. Pfister;P. Wolfrum;D. E. Rivera;S. Deshpande
通讯作者：
S. Deshpande