Learning Patient-Specific Models from Clinical Data

描述（由申请人提供）： 诊断患者的状况并预测患者的结果是临床护理中的关键活动。这些活动执行得越好，患者的医疗保健结果就可能越好。类似地，医疗资源的有效利用取决于能够准确地确定资源可能在何时何地有用，这也涉及进行预测。即使是预测准确性的适度改善，有时也会在改善患者预后和降低医疗成本方面产生重大的医疗后果。因此，对于患者个人和整个社会来说，做出准确的医疗保健预测是一项重要的任务。 有一个明显的趋势，越来越多的病人数据，可在医疗保健信息系统和临床研究的数据库的电子。因此，有机会从这些数据源构建基于证据的预测模型。事实上，已经构建了许多临床预测模型。通常情况下，一个单一的预测模型已经从数据中归纳出来，然后应用于-相关-每个未来的病人。构建这样的群体范围的模型以尝试优化其对应用于其的所有未来患者病例的平均预测性能。模型归纳没有利用手头的患者病例信息，这可能会增强所构建模型的形式和内容。 目前的项目将研究构建对患者信息敏感的患者特异性模型的方法。这些新方法将评估其在脓毒症，心力衰竭和物质使用障碍的不同领域预测患者结局的性能。各种目前的人口范围内的模型构建方法，包括逻辑回归和神经网络，将作为控制。 如果患者特异性模型可以改善全人群模型的预测性能，这似乎是合理的，那么作为未来基于计算机的临床护理决策支持系统的组成部分，增加的性能可能会提供实质性的益处。

DESCRIPTION (provided by applicant): Diagnosing a patient's condition and predicting the patient's outcomes are critical activities in clinical care. The better these activities can be performed, the better the patient's healthcare results are likely to be. Similarly, efficient use of healthcare resources depends upon being able to determine accurately when and where a resource is likely to be useful, which also involves making predictions. Even modest improvements in predictive accuracy can at times have significant healthcare consequences in terms of improved patient outcomes and reduced healthcare costs. Thus, for both individual patients and for society at large, making accurate healthcare predictions is an important task. There is a clear trend toward having more and more patient data that is available electronically in healthcare information systems and in databases of clinical research studies. Thus, there is an opportunity to construct evidence-based predictive models from these data sources. Indeed, numerous clinical prediction models have already been constructed. Typically, a single prediction model has been induced from the data, and then applied -- as relevant -- to each future patient. Such a population-wide model is constructed to try to optimize its average predictive performance on all future patient cases to which it is applied. Model induction does not take advantage of information about the patient case at hand, which could potentially enhance the form and content of the model that is constructed. The current project will investigate methods that construct patient-specific models that are sensitive to information about the patient at hand. These new methods will be evaluated for their performance in predicting patient outcomes in the diverse domains of sepsis, heart failure and substance use disorders. A variety of current population-wide model-construction methods, including logistic regression and neural networks, will serve as controls. If patient-specific models can improve upon the predictive performance of population-wide models, as seems plausible, the added performance is likely to provide substantial benefit as a component of future computer-based decision-support systems for clinical care.