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Decision Support in the Care of Preterm Newborns-Tool Development

早产儿护理中的决策支持-工具开发

基本信息

批准号：
7665362
负责人：
MARTINA MUELLER
金额：
$ 18.54万
依托单位：
MEDICAL UNIVERSITY OF SOUTH CAROLINA
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-08-01 至 2012-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7665362
关键词：
Adverse effects Arts Biological Neural Networks Caring Characteristics Clinical Committee Members Communities Computer software Data Data Storage and Retrieval Decision Making Decision Trees Development Evaluation Extensible Markup Language Health Care Costs Infant Information Resources Management Internet Language Logistic Regressions Machine Learning Measures Mechanical Ventilators Mechanical ventilation Methods Modeling Nature Neonatal Neonatal Intensive Care Units Neural Network Simulation Newborn Infant Online Systems Outcome Performance Premature Infant Research Infrastructure Research Personnel Resources Risk System Translational Research Ventilator Work caregiving clinical practice data exchange data mining data sharing data structure experience interoperability mathematical model member neonate premature prototype public health relevance respiratory distress syndrome routine care success tool tool development web based interface

项目摘要

DESCRIPTION (provided by applicant): Even after many advances in ventilator management, prediction of extubation outcome for a mechanically ventilated premature infant with respiratory distress syndrome (RDS) remains a challenging task for clinicians. We recently developed a machine-learned model (an Artificial Neural Network, ANN) to assist in decision- making regarding extubation of premature newborns (Mueller et al., 2004, 2006). The ANN model was found to perform with accuracy comparable to that of experienced clinicians; however, this approach needs to be compared to equally powerful machine-learning approaches before it can be evaluated in clinical practice. An appropriately validated decision-support tool could help in reducing the number of days a premature infant spends on a mechanical ventilator, and hence the risk of developing short and long-term side effects of mechanical ventilation, resulting in a corresponding decrease in overall health care costs. In this R21 proposal, we will use several machine-learning approaches combined as a committee formation to obtain the best prediction of extubation success for a given infant. Further, we will build on the previously developed ANN prototype to create an enhanced decision support tool by developing data representation, storage, management, and most important, causal inference, which will enable effective integration of the resulting web-based decision-support tool with clinical practice. This last feature is only possible due to the integrated nature of the proponents themselves, which range from data structure and mathematical modeling experts to experienced neonatologists with a well established working relationship. The proposed effort aims at using advanced modeling tools for translational research by developing a web-based decision-support tool to aid primarily inexperienced clinicians in their decision-making and by promoting interoperability and data exchange among researchers in this field. The critical feature of this infrastructure is its web-based nature, which enables clinicians to evaluate a predictor's accuracy and parametric sensitivity individually for each neonate without having to use any other software than a web-browser. Such a prediction model will be of critical value not only to increase overall clinical accuracy but also to identify effective measures of validity of the original predictions. The overall aim of this study is to develop a high performing web-based prediction system to use as a decision-support tool in clinical practice and to promote interoperability, and thus, data sharing and interaction among researchers in the neonatal community. PUBLIC HEALTH RELEVANCE: Predicting extubation outcome in premature infants on mechanical ventilators remains a challenging task even for experienced clinicians. In the proposed work, we aim to provide a sophisticated web-based tool that uses a machine-learning committee comprised of artificial neural networks (ANN), support vector machines (SVM), naive Bayesian classifiers (NBC), influence diagrams (ID), boosted decision trees (BDT) and multivariable logistic regression (MLR) to assist primarily inexperienced clinicians in the decision-making. For the implementation of this tool we propose to develop an XML schema and RDFS model that can promote interoperability, and thus, data sharing and interaction among researchers in the neonatal community.

描述（由申请人提供）：即使在呼吸机管理方面取得了许多进展，预测机械通气早产儿呼吸窘迫综合征（RDS）的拔管结局仍然是临床医生面临的一项挑战性任务。我们最近开发了一种机器学习模型（人工神经网络，ANN），以帮助做出关于早产儿拔管的决策（Mueller等人，2004年，2006年）。人工神经网络模型的准确性与经验丰富的临床医生相当;然而，这种方法需要与同样强大的机器学习方法进行比较，然后才能在临床实践中进行评估。适当验证的决策支持工具可以帮助减少早产儿使用机械通气的天数，从而减少机械通气的短期和长期副作用的风险，从而相应降低整体医疗保健成本。在这个R21提案中，我们将使用多种机器学习方法结合起来作为委员会的组成，以获得特定婴儿拔管成功的最佳预测。此外，我们将建立在以前开发的人工神经网络原型，通过开发数据表示，存储，管理，最重要的是，因果推理，这将使有效地整合所产生的基于网络的决策支持工具与临床实践，以创建一个增强的决策支持工具。这最后一个功能是唯一可能的，由于支持者本身的综合性质，其中包括数据结构和数学建模专家，经验丰富的数学家，建立了良好的工作关系。拟议的努力旨在通过开发基于网络的决策支持工具，以帮助主要是没有经验的临床医生在他们的决策，并通过促进互操作性和数据交换研究人员在这一领域的转化研究中使用先进的建模工具。该基础设施的关键特征是其基于网络的性质，这使得临床医生能够为每个新生儿单独评估预测器的准确性和参数灵敏度，而无需使用除网络浏览器之外的任何其他软件。这样的预测模型将是至关重要的价值，不仅提高整体的临床准确性，而且还确定有效的措施，有效性的原始预测。本研究的总体目标是开发一个高性能的基于网络的预测系统，在临床实践中作为一个决策支持工具，并促进互操作性，从而在新生儿社区的研究人员之间的数据共享和互动。公共卫生关系：即使对于经验丰富的临床医生来说，预测机械呼吸机上早产儿的拔管结果仍然是一项具有挑战性的任务。在拟议的工作中，我们的目标是提供一个复杂的基于网络的工具，使用由人工神经网络（ANN），支持向量机（SVM），朴素贝叶斯分类器（NBC），影响图（ID），提升决策树（BDT）和多变量逻辑回归（MLR）组成的机器学习委员会，以帮助主要是缺乏经验的临床医生进行决策。为了实现这个工具，我们建议开发一个XML模式和RDFS模型，可以促进互操作性，从而在新生儿社区的研究人员之间的数据共享和互动。