Decision Support in the Care of Preterm Newborns-Tool Development

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，对患有呼吸窘迫综合征（RDS）的机械通气早产儿拔管结果的预测仍然是临床医生的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），以协助早产儿拔管的决策（Mueller等人，2004,2006）。发现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，在临床实践中评估这种方法之前，需要将其与同样强大的机器学习方法进行比较。一个经过适当验证的决策支持工具可以帮助减少早产儿在机械呼吸机上花费的天数，从而减少机械通气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。

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.