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Using Machine Learning to Predict Clinical Deterioration in Hospitalized Children

使用机器学习预测住院儿童的临床恶化

基本信息

批准号：
10645029
负责人：
Anoop Mayampurath
金额：
$ 14.76万
依托单位：
UNIVERSITY OF WISCONSIN-MADISON
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-15 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10645029
关键词：
Admission activity Adult Advisory Committees Applications Grants Area Cardiopulmonary Caring Chicago Child Childhood Clinical Clinical Data Clinical Trials Complex Data Data Analyses Data Element Data Set Detection Deterioration Doctor of Philosophy Early Intervention Ecosystem Electronic Health Record Environmental Risk Factor Event Frequencies Future Goals Grant Health Heart Arrest Hospital Costs Hospital Mortality Hospitalized Child Hospitals Hour Hylobates Genus Intensive Care Units Intervention Knowledge Laboratories Location Machine Learning Maps Mentorship Methodology Modality Modeling Monitor Natural Language Processing Neurological outcome Outcome Patient Care Patients Pediatric Hospitals Performance Pharmaceutical Preparations Physiological Process Publishing Randomized, Controlled Trials Research Personnel Risk Risk Factors Statistical Models Structure Techniques Testing Text Time Training Universities Validation Work career development clinical decision support clinical predictive model clinical predictors clinical risk clinically relevant cohort cost electronic structure experience high risk improved improved outcome machine learning framework mortality mortality risk pediatric patients predictive modeling prevent preventable death research and development risk prediction risk prediction model statistics tool trend unstructured data ward

项目摘要

PROJECT SUMMARY Children who are admitted to the hospital and experience deterioration have a high risk of mortality and poor long-term health. Current warning early scores indicating risk of deterioration are subjectively derived and have not reduced in-hospital mortality. In recent work, I developed a vital sign based statistical model that demonstrated improved accuracy over current risk scores at predicting clinical deterioration in hospitalized children 24 hours in advance. Within adults, the combination of longitudinal data analysis techniques, machine learning, and electronic health record (EHR) data have led to highly accurate early warning scores. Therefore, my aim in this grant proposal is to utilize longitudinal integration of EHR data in a machine learning framework to develop a model for predicting clinical deterioration in hospitalized children as early as possible. I will do this by first deriving and validating a prediction model using structured EHR data collected from three pediatric hospitals (Aim 1). Using the same cohort, I will then build and validate a prediction model using features derived from unstructured clinical notes (Aim 2). I will also compare if the addition of unstructured features improves the prediction accuracy of the model derived in Aim 1. Finally, I will determine the association between non-patient level environmental variables within the hospital ecosystem and risk of clinical deterioration in hospitalized children (Aim 3). I will also determine if the addition of these environmental risk factors improves performance of the prediction model derived through Aims 1 and 2. Completion of this proposal will result in a validated pediatric risk prediction model that will enable clinicians to recognize early signs of deterioration in hospitalized children. This will facilitate timely intervention, thereby saving lives and improving long-term health. In addition, this grant will also provide me with crucial data for a future R01 trial aimed at assessing the impact of the prediction model in reducing mortality, decreasing costs, and improving long-term outcomes in hospitalized children. To establish myself as an independent investigator in pediatric prediction modeling, I propose a training plan that includes comprehensive didactics and mentorship in the areas of longitudinal data analysis, advanced machine learning, natural language processing, and concepts in pediatric care. I have assembled a first-class mentorship team comprised of national experts in longitudinal data analysis techniques and EHR-based machine learning (Robert Gibbons PhD and Matthew Churpek MD, PhD). My advisory team is comprised of experts in natural language processing (Dmitriy Dligach PhD), clinical decision support around deterioration events (Dana Edelson MD, MS and Priti Jani MD), and pediatric early warning scores (Christopher Parshuram MB., ChB., D. Phil., FRACP). By completing my research and career development goals, I will develop into an independent expert investigator in developing pediatric prediction models for ultimately improving outcomes in hospitalized children.

项目摘要入院并经历恶化的儿童死亡风险高，长期健康。目前表明恶化风险的预警早期评分是主观得出的，而不是降低住院死亡率。在最近的工作中，我开发了一个基于生命体征的统计模型，在预测住院患者临床恶化方面，孩子们提前24小时在成年人中，纵向数据分析技术、机器学习和电子健康记录（EHR）数据已经导致高度准确的预警分数。因此，我们认为，我在这项拨款申请中的目标是在机器学习框架中利用EHR数据的纵向集成建立一个尽早预测住院儿童临床恶化的模型。我既这样行首先使用从三个儿科患者收集的结构化EHR数据推导和验证预测模型，医院（目标1）。使用相同的队列，然后我将使用特征构建和验证预测模型来源于非结构化临床记录（目标2）。我还将比较是否添加非结构化功能提高了目标1中导出的模型的预测精度。最后，我将确定协会医院生态系统内的非患者级环境变量与临床风险之间的关系住院儿童病情恶化（目标3）。我还将确定这些环境风险的增加是否这些因素提高了通过目标1和目标2得出的预测模型的性能。完成本该提案将产生一个经过验证的儿科风险预测模型，使临床医生能够早期识别住院儿童病情恶化的迹象。这将有助于及时干预，从而挽救生命，改善长期健康。此外，这笔赠款还将为我提供未来R01试验的关键数据旨在评估预测模型在降低死亡率、降低成本和改善住院儿童的长期结局。使自己成为一名独立的儿科研究员预测建模，我提出了一个培训计划，其中包括全面的教学和指导，纵向数据分析、高级机器学习、自然语言处理和儿科护理我组建了一个由全国纵向专家组成的一流导师团队，数据分析技术和基于EHR的机器学习（Robert Gibbons PhD和Matthew Churpek MD， PhD）。我的顾问团队由自然语言处理（Dmitriy Dligach博士），临床关于恶化事件的决策支持（Dana Edelson MD、MS和Priti Jani MD），以及儿科早期警告分数（Christopher Parshuram MB.，ChB.，D.菲尔FRACP）。通过完成我的研究和事业发展目标，我将发展成为一个独立的专家研究员，在发展儿科预测最终改善住院儿童预后的模型。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Machine Learning and Clinical Informatics for Improving HIV Care Continuum Outcomes.

DOI：
10.1007/s11904-021-00552-3
发表时间：
2021-06
期刊：
Current HIV/AIDS reports
影响因子：
4.6
作者：
Ridgway JP;Lee A;Devlin S;Kerman J;Mayampurath A
通讯作者：
Mayampurath A

Prediction of Intracranial Hypertension and Brain Tissue Hypoxia Utilizing High-Resolution Data from the BOOST-II Clinical Trial.

DOI：
10.1089/neur.2022.0055
发表时间：
2022
期刊：
NEUROTRAUMA REPORTS
影响因子：
2.4
作者：
Lazaridis, Christos;Ajith, Aswathy;Mansour, Ali;Okonkwo, David O.;Diaz-Arrastia, Ramon;Mayampurath, Anoop;BOOST II Investigators, B. O. O. S. T. I. I. Investigators
通讯作者：
BOOST II Investigators, B. O. O. S. T. I. I. Investigators

Multiple Organ Dysfunction Interactions in Critically Ill Children.

DOI：
10.3389/fped.2022.874282
发表时间：
2022
期刊：
FRONTIERS IN PEDIATRICS
影响因子：
2.6
作者：
Badke, Colleen M.;Mayampurath, Anoop;Sanchez-Pinto, L. Nelson
通讯作者：
Sanchez-Pinto, L. Nelson

Mortality and PICU Hospitalization Among Pediatric Gunshot Wound Victims in Chicago.

DOI：
10.1097/cce.0000000000000626
发表时间：
2022-03
期刊：
Critical care explorations
影响因子：
0
作者：
Rhine MA;Hegermiller EM;Kane JM;Slidell MB;Mayampurath A;McQueen AA;Mbadiwe N;Pinto NP
通讯作者：
Pinto NP

PICU Survivorship: Factors Affecting Feasibility and Cohort Retention in a Long-Term Outcomes Study.

DOI：
10.3390/children9071041
发表时间：
2022-07-13
期刊：
CHILDREN-BASEL
影响因子：
2.4
作者：
Sobotka, Sarah A.;Lynch, Emma J.;Dholakia, Ayesha, V;Mayampurath, Anoop;Pinto, Neethi P.
通讯作者：
Pinto, Neethi P.