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

• 批准号: 7530584
• 负责人: MARTINA MUELLER
• 金额: $ 23.42万
• 依托单位: MEDICAL UNIVERSITY OF SOUTH CAROLINA
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7530584
• 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; Newborn Respiratory Distress Syndrome; Numbers; Online Systems; Outcome; Performance; Premature Infant; Public Health; Purpose; Range; Research Infrastructure; Research Personnel; Resources; Risk; System; Translational Research; Ventilator; Work; caregiving; data mining; data structure; day; experience; interoperability; mathematical model; member; neonate; prototype; success; tool; tool development; web based interface

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）：即使在呼吸机管理的许多进展之后，对机械通气的早产婴儿（RDS）的拔管预测预测仍然是临床医生的一项艰巨任务。我们最近开发了一种机器学习模型（人工神经网络，ANN），以协助制定有关拔管早产新生儿的决策（Mueller等，2004，2006）。发现ANN模型具有与经验丰富的临床医生相当的准确性。但是，需要将这种方法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。经过适当验证的决策支持工具可以帮助减少早产婴儿在机械呼吸机上花费的天数，从而产生机械通气的短期和长期副作用的风险，从而导致整体医疗费用降低。 在此R21提案中，我们将使用几种机器学习方法合并为委员会成立，以获得给定婴儿的拔管成功的最佳预测。此外，我们将基于先前开发的ANN原型，以通过开发数据表示，存储，管理和最重要的因果推理来创建增强的决策支持工具，这将使所得的基于Web的决策支持工具与临床实践有效整合。仅由于支持者本身的综合性质，其最后一个功能才是可能的，范围从数据结构和数学建模专家到具有良好的工作关系的经验丰富的新生儿学家。拟议的努力旨在通过开发基于网络的决策支持工具来使用高级建模工具来进行翻译研究，以帮助主要没有经验的临床医生进行决策，并促进该领域研究人员之间的互操作性和数据交流。该基础架构的关键特征是其基于网络的性质，它使临床医生能够分别评估每个新生儿的预测器的准确性和参数灵敏度，而无需使用除Web浏览器以外的任何其他软件。这样的预测模型不仅要提高总体临床准确性，而且还将是确定原始预测有效性的有效度量的关键值。 这项研究的总体目的是开发高性能的基于Web的预测系统，以用作临床实践中的决策支持工具，并促进新生儿社区研究人员之间的数据共享和互动。 公共卫生相关性：即使对于经验丰富的临床医生，预测机械呼吸机早产婴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，我们旨在提供一个基于网络的基于网络的工具，该工具使用机器学习委员会（ANN），支持向量机（SVM），天真的贝叶斯分类器（NBC），影响图图（ID），促进的决策树（BDT）（BDT）和多个logistic Recorment Intrescect Incortion Incortion Incories Incianialialization Insikialization insex insex insex，为了实施此工具，我们建议开发一个XML模式和RDFS模型，该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进行数据共享和互动。