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ASSESSING NEW MATHEMATICAL MODELS FOR MEDICAL EVENTS

评估医疗事件的新数学模型

基本信息

批准号：
6185210
负责人：
JOHN GRIFFITH
金额：
$ 41.67万
依托单位：
TUFTS MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
1995
资助国家：
美国
起止时间：
1995-01-01 至 2002-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/6185210
关键词：
artificial intelligence computational neuroscience computer assisted medical decision making computer simulation health care facility information system human data information system analysis mathematical model model design /development outcomes research prognosis statistics /biometry

项目摘要

Predictive models that generate estimate probabilities for medical outcomes have become widely used in health services research, in health policy, and increasingly, for the assessment of health care and for real-time decision support. Logistic regression models for medical events are central to most probabilistic predictive clinical decision aids and are fundamental to comparative analyses of medical care based on risk-adjusted events. In such applications, inaccurate assessment of patient risk can have significant health care and health policy implications. New computer-based modeling techniques including generalized additive models, classification trees, and neural networks may potentially capture information that regression methods may miss or misrepresent. However, these methods use very local information in model construction and may be overfit to the sample data and thus not transport well to new settings. In years 1-3, we investigated the relative accuracy of predictions made by these modeling methods under a variety of data structures, including the presence of outliers and missing data. For many of these data structures we found that the more "local" procedures frequently did not generalize to new test data as well as traditional regression methods. However, our results suggest that as sample size and data complexity increases the performance of these procedures may substantially improved. Thus, to test these findings under more general conditions, we now propose two additional years of research to 1) rigorously assess the relative predictive performance and transportability of other new innovative modeling methods and of original hybrid model construction methods; 2) systematically investigate the relative predictive performance and model transportability of modeling methods applied to large and complex data structures; and 3) explore and assess procedures for handling outliers and missing data for classification trees and neural networks. The completion of the proposed work will result in the first systematic exploration of the factors affecting the predictive performance of the major modeling methods used to predict medical outcomes, and the comparative performance of models constructed by these methods on the extremely large data sets of the type that are becoming increasing available to researchers.

预测模型产生的估计概率的医疗结果已被广泛用于卫生服务研究，在卫生政策，并越来越多地用于评估医疗保健和实时决策支持。医疗事件的逻辑回归模型是大多数概率预测临床决策辅助工具的核心，也是基于风险调整事件的医疗护理比较分析的基础。在这些应用中，对患者风险的不准确评估可能会对医疗保健和卫生政策产生重大影响。新的基于计算机的建模技术，包括广义加性模型、分类树和神经网络，可能会捕获回归方法可能遗漏或误报的信息。然而，这些方法在模型构建中使用非常局部的信息，并且可能过拟合样本数据，因此不能很好地传输到新的设置。在第1-3年，我们研究了在各种数据结构下，包括离群值和缺失数据的存在下，这些建模方法预测的相对准确性。对于这些数据结构中的许多，我们发现，更多的“本地”程序往往不能推广到新的测试数据以及传统的回归方法。然而，我们的研究结果表明，随着样本量和数据复杂性的增加，这些程序的性能可能会大大提高。因此，为了在更一般的条件下测试这些发现，我们现在建议再进行两年的研究：1）严格评估其他新的创新建模方法和原始混合模型构造方法的相对预测性能和可移植性; 2）系统地研究应用于大型复杂数据结构的建模方法的相对预测性能和模型可移植性;以及3）探索和评估处理分类树和神经网络的离群值和缺失数据的程序。拟议工作的完成将导致第一次系统地探索影响用于预测医疗结果的主要建模方法的预测性能的因素，以及通过这些方法构建的模型在研究人员越来越多的超大型数据集上的比较性能。