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）：即使在呼吸机管理方面取得了许多进展，预测患有呼吸窘迫综合征（RDS）的机械通气早产儿的拔管结果对于临床医生来说仍然是一项具有挑战性的任务。我们最近开发了一种机器学习模型（人工神经网络，ANN）来协助有关早产儿拔管的决策（Mueller 等人，2004 年，2006 年）。研究发现 ANN 模型的准确度可与经验丰富的临床医生相媲美；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。经过适当验证的决策支持工具可以帮助减少早产儿使用机械呼吸机的天数，从而减少机械通气产生短期和长期副作用的风险，从而相应降低总体医疗保健成本。 在此 R21 提案中，我们将使用多种机器学习方法结合起来组成委员会，以获得特定婴儿拔管成功的最佳预测。此外，我们将在之前开发的 ANN 原型的基础上，通过开发数据表示、存储、管理以及最重要的因果推理来创建增强的决策支持工具，这将使基于网络的决策支持工具与临床实践有效集成。最后一个功能之所以成为可能，是因为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以及具有良好工作关系的经验丰富的新生儿学家。拟议的工作旨在通过开发基于网络的决策支持工具来帮助主要缺乏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换，从而使用先进的建模工具进行转化研究。该基础设施的关键特征是其基于网络的性质，它使临床医生能够单独评估每个新生儿的预测器准确性和参数敏感性，而无需使用网络浏览器之外的任何其他软件。这样的预测模型不仅对于提高整体临床准确性而且对于确定原始预测有效性的有效衡量标准都具有重要价值。 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，作为临床实践中的决策支持工具，并促进互操作性，从而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来说，预测机械呼吸机早产儿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，该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）、增强决策树（BDT）和多变量逻辑回归（MLR）组成的机器学习委员会来帮助主要缺乏经验的临床医生做出决策。为了实现该工具，我们建议开发一个 XML 模式和 RDFS 模型，以促进互操作性，从而促进新生儿社区研究人员之间的数据共享和交互。