Mechanistic Machine Learning

机械机器学习

基本信息

批准号：
9767278
负责人：
David J. Albers
金额：
$ 66.06万
依托单位：
UNIVERSITY OF COLORADO DENVER
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-20 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9767278
关键词：
Address Admission activity Affect Area Assimilations Carbohydrates Clinical Clinical Treatment Complex Computational Biology Data Data Science Depressed mood Early Intervention Eating Endocrine Physiology Evaluation Fingers Food Future Glomerular Filtration Rate Glucose Goals Health Healthcare Hepatic Hour Insulin Insulin Infusion Systems Insulin-Dependent Diabetes Mellitus Intensive Care Units Intervention Knowledge Lead Learning Link Machine Learning Manuals Markov chain Monte Carlo methodology Measurable Measurement Measures Medical Medicine Methodology Methods Minor Planets Modeling Monitor Non-Insulin-Dependent Diabetes Mellitus Outcome Outpatients Patient-Focused Outcomes Patients Phenotype Physicians Physiological Physiology Power Plants Property Publishing Recommendation Renal function Running System Techniques Technology Testing Time Training Transportation Treatment outcome Uncertainty Validity of Results Weather Work base clinical phenotype computerized control theory fly glucose metabolism glucose monitor glucose production health record improved individual patient insight insulin secretion interstitial mathematical model nutrition optimal treatments outcome forecast outcome prediction personalized learning personalized medicine physiologic model predictive modeling reduced food intake space travel treatment strategy

项目摘要

PROJECT SUMMARY / ABSTRACT The goal of this project is to combine empirical data with mechanistic physiologic knowledge to produce personalized, quantitative predictions that can lead to improved treatments. In normal practice, physicians reason by analogy from generic physiologic principles, but the technology exists to exploit even imperfect physiologic models make treatment personalized and quantitatively grounded in physiology, and to improve learning from empirical data. We will apply data assimilation (DA), mechanistic mathematical modeling, machine learning, and control theory, which have revolutionized space travel, weather forecasting, transportation and flight, and manufacturing. Data assimilation and control theory have seen very limited use in medicine, usually applied in data-rich circumstances like continuous glucose monitoring or packemakers. Our previous work demonstrated use of data assimilation with glucose-insulin models to predict glucose in the outpatient type 2 diabetes setting. We will extend data assimilation and control theory using, for example, a constrained ensemble Kalman filter and an offline Markov Chain Monte Carlo algorithm, to better handle sparse, short training sets on rapidly changing patients, and we will apply it in the setting of glucose management in the intensive care unit (ICU). Moreover, we will develop DA for phenotyping applications by exploiting the parameter estimation capabilities of DA. Data assimilation can be used to estimate measureable and unmeasureable physiologic states and parameters, and we will use these estimates to create higher definition phenotypes. While we are focusing on glucose management in the ICU, we will develop methods that are likely to generalize, beginning the effort to develop DA in the context of healthcare more broadly. The work we propose is a necessary step toward being able to use mechanism-driven DA to test, validate and optimize personalized short-term treatment strategies, long-term health forecasts, and mechanistic physiologic understanding. We will carry out the following aims: AIM 1—forecast—extend the DA methodology to allow forecasting, personalization, model evaluation, and model selection in the ICU context, relating treatment input to physiologic outcome; AIM 2—phenotype—extend the DA framework to state and parameter estimation to allow for mechanism-based phenotyping, careful uncertainty quantification, and inference of difficult or impossible-to-measure physiology; AIM 3—control—extend the DA to include a controller that begins with desired clinical outcomes, e.g., glucose range, and estimates the inputs, e.g., insulin or nutrition, required to achieve the outcomes.

项目概要/摘要该项目的目标是将经验数据与机械生理知识相结合，产生在正常实践中，医生可以进行个性化的定量预测，从而改善治疗。从一般生理原理进行类比推理，但技术的存在可以利用甚至不完美的生理模型使治疗个性化并以生理学为基础定量化，并改善我们将应用数据同化（DA）、机械数学建模、机器学习和控制理论彻底改变了太空旅行、天气预报、运输和飞行，以及数据同化和控制理论的应用非常有限。在医学中，通常应用于数据丰富的环境，例如连续血糖监测或包装制造商。我们之前的工作证明了使用葡萄糖-胰岛素模型的数据同化来预测血糖我们将使用例如数据同化和控制理论来扩展门诊 2 型糖尿病设置。约束集成卡尔曼滤波器和离线马尔可夫链蒙特卡罗算法，以更好地处理针对快速变化的患者的稀疏、简短的训练集，我们将其应用于葡萄糖的设置此外，我们将开发用于表型分析应用的 DA。利用 DA 的参数估计功能可用于估计可测量值。以及无法测量的生理状态和参数，我们将使用这些估计来创建更高的当我们专注于 ICU 的血糖管理时，我们将开发一些方法可能会推广，开始在更广泛的医疗保健背景下开发 DA 工作。我们建议这是能够使用机制驱动的 DA 来测试、验证和优化的必要步骤个性化短期治疗策略、长期健康预测和机械生理学理解。我们将实现以下目标： AIM 1——预测——扩展 DA 方法以进行预测， ICU 环境中的个性化、模型评估和模型选择，将治疗输入与生理结果；AIM 2—表型—将 DA 框架扩展到状态和参数估计允许基于机制的表型分析、仔细的不确定性量化以及难度或无法测量的生理学；AIM 3—控制—将 DA 扩展为包括以下开头的控制器：期望的临床结果，例如血糖范围，并估计所需的输入，例如胰岛素或营养取得成果。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Clinical Decision Support for Traumatic Brain Injury: Identifying a Framework for Practical Model-Based Intracranial Pressure Estimation at Multihour Timescales.

DOI：
10.2196/23215
发表时间：
2021-03-22
期刊：
JMIR medical informatics
影响因子：
3.2
作者：
Stroh JN;Bennett TD;Kheyfets V;Albers D
通讯作者：
Albers D

Personalization and Pragmatism: Pediatric Intracranial Pressure and Cerebral Perfusion Pressure Treatment Thresholds.

DOI：
10.1097/pcc.0000000000002637
发表时间：
2021-02-01
期刊：
Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
影响因子：
0
作者：
Stroh JN;Albers DJ;Bennett TD
通讯作者：
Bennett TD

Neural Networks for Mortality Prediction: Ready for Prime Time?

DOI：
10.1097/pcc.0000000000002710
发表时间：
2021-06-01
期刊：
Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
影响因子：
0
作者：
Bennett TD;Russell S;Albers DJ
通讯作者：
Albers DJ

Reduced model for female endocrine dynamics: Validation and functional variations.

女性内分泌动力学的简化模型：验证和功能变化。

DOI：
10.1016/j.mbs.2023.108979
发表时间：
2023
期刊：
Mathematical biosciences
影响因子：
4.3
作者：
Graham,EricaJ;Elhadad,Noémie;Albers,David
通讯作者：
Albers,David

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David J. Albers其他文献

Scaling Up HCI Research: from Clinical Trials to Deployment in the Wild.

扩大人机交互研究：从临床试验到野外部署。

DOI：
发表时间：
2021
期刊：
CHI Extended Abstracts
影响因子：
0
作者：
Lena Mamykina;Arlene M. Smaldone;Suzanne R. Bakken;Noémie Elhadad;Elliot G. Mitchell;Pooja M. Desai;Matthew E. Levine;Jonathan N. Tobin;Andrea Cassells;Patricia G. Davidson;David J. Albers;G. Hripcsak
通讯作者：
G. Hripcsak