Statistical Learning and Adaptive Observation in Clinical Prediction: Methodology and Applications

临床预测中的统计学习和自适应观察：方法论和应用

• 批准号: 2285737
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2285737
• 关键词: Statistical; Learning; Adaptive; Observation; Clinical

Project Description: (maximum of 4,000 characters)Clinical prediction models (CPMs), which predict the occurrence of an event of interest given what is known about an individual, could enable a personalized and preventative approach to healthcare. However, existing CPMs are usually based on cross-sectional data, thereby ignoring the rich longitudinal information in medical records. As such, exploring ways of incorporating longitudinal data into CPMs is an active area of research. One problem is that the location/setting of where longitudinal observations are recorded (e.g. primary care or secondary care) can influence the recorded observations, which can introduce bias into the CPMs if not properly accounted for. Additionally, limited resources restrict how frequently healthcare professionals can observe longitudinal information about patients; in principle, CPMs could be used to triage adaptive periods of high- and low-frequency observation for patients, but methodological research is required before this can be done in practice. Therefore, this PhD project will develop statistical methods to allow the derivation of CPMs that "learn" from patient-level behaviours and observation patterns. The project has initially two avenues of investigation. Firstly, we will explore if knowing how and where a risk factor has been measured leads to improved prediction of clinical outcomes (measurement error perspective). Secondly, we will look to develop probabilistic modelling frameworks to evaluate whether there are heterogeneous groups of patients who need stratified/personalised interventions. Additional areas of investigation will be data sample size, prediction paradox, informed presence.

项目名称：（最多4，000个字符）临床预测模型（CPM）可以根据对个人的了解预测感兴趣事件的发生，从而实现个性化和预防性的医疗保健方法。然而，现有的CPM通常是基于横截面数据，从而忽略了丰富的纵向信息的病历。因此，探索将纵向数据纳入CPM的方法是一个活跃的研究领域。一个问题是，记录纵向观察的位置/设置（例如，初级护理或二级护理）可能会影响记录的观察结果，如果没有适当考虑，可能会在CPM中引入偏倚。此外，有限的资源限制了医疗保健专业人员观察患者纵向信息的频率;原则上，CPM可用于对患者的高频和低频观察的适应期进行分类，但在此之前需要进行方法学研究实践。因此，本博士项目将开发统计方法，以允许从患者水平的行为和观察模式中“学习”CPM的推导。该项目最初有两种调查途径。首先，我们将探讨了解如何以及在何处测量风险因素是否会改善对临床结局的预测（测量误差观点）。其次，我们将寻求开发概率建模框架，以评估是否存在需要分层/个性化干预的异质性患者组。调查的其他领域将是数据样本大小，预测悖论，知情的存在。