Improving Statistical Machine Learning approaches for Time-to-Event Prediction Modelling

改进事件时间预测建模的统计机器学习方法

• 批准号: 2722161
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2722161
• 关键词: Improving; Statistical; Machine; Learning; approaches

BackgroundThe implications of more than one chronic health condition occurring in the same individual on long-term health outcomes is often unclear. Existing studies typically focus on the association of a single condition with a single outcome and often ignore or select out individuals who might have a background of multiple long-term conditions (MLTC). Consequently, the health needs of certain groups of individuals with MLTCs may not be adequately addressed. One obstacle to studying the impact of MLTC is the number of possible combinations of health conditions. It may not be feasible to identify and recruit sufficiently large numbers of individuals with certain sets of conditions to study.To overcome these challenges, a recent approach has been to retrospectively use information held in medical record databases. Groups of individuals with similar characteristics can be identified from the database and compared with other groups to determine why certain health outcomes manifest. Computer models, based on statistical machine learning algorithms, can then be created to predict the future risk of these health outcome given individual characteristics. However, the use of this historical datasets to create prediction models needs careful handling. The data may have been acquired under a different context and/or premise to the situation in which you may be interested, and this could lead to computer models that give biased or misleading insights. In addition, complex machine learning models can lack robustness and be prone to unstable behaviour, for example, giving very different risk probabilities for two nearly identical patients.Aims & ObjectivesThis research aims to develop methodologies that will improve the robustness and validity of statistical machine learning-based prediction models that are constructed from observational data:1) To assess the robustness and stability of existing statistical machine learning approaches for time-to-event modelling,2) To develop methodology to improve aspects of the robustness and stability of statistical machine learning approaches for time-to-event modelling,3) To test the novel methodology using real-world primary care data and compare it to existing approaches.Novelty of the research methodologyStandard machine learning development focuses on the use of accuracy-related criteria to measure how well prediction models perform. However, there is increasing awareness that in real-world usage, accuracy is just one of several important criteria that determines the usefulness of a prediction model. In this research we will study the use of model training criteria that encompass considerations of the (i) four levels of model stability (as defined in Riley & Collins (2023), (ii) consistency between model versions after updating, and (iii) sensitivity to unusual data inputs.Alignment to EPSRC's strategies and research areasThis project falls within the 'EPSRC Healthcare Technologies research area' where "Optimising disease prediction, diagnosis and intervention" is one of the themes or research areas listed on this website https://www.epsrc.ac.uk/research/ourportfolio/themes/It will create new methods for analysing large real-world primary care health data sets, underpin patient-specific predictive models, and support the identification of opportunities for prevention of disease or its recurrence.CollaborationsThis project will involve a collaboration with the University of Birmingham.

背景同一个人发生不止一种慢性健康状况对长期健康结果的影响通常尚不清楚。现有的研究通常关注单一病症与单一结果的关联，并且经常忽略或选择可能具有多种长期病症（MLTC）背景的个体。因此，某些患有 MLTC 的人群的健康需求可能无法得到充分满足。研究 MLTC 影响的障碍之一是可能的健康状况组合的数量。识别和招募足够多具有某些条件的个体来进行研究可能是不可行的。为了克服这些挑战，最近的一种方法是回顾性地使用医疗记录数据库中保存的信息。可以从数据库中识别具有相似特征的个体群体，并将其与其他群体进行比较，以确定为什么会出现某些健康结果。然后可以创建基于统计机器学习算法的计算机模型，以根据个人特征预测这些健康结果的未来风险。然而，使用这些历史数据集来创建预测模型需要谨慎处理。这些数据可能是在与您可能感兴趣的情况不同的背景和/或前提下获取的，这可能会导致计算机模型给出有偏见或误导性的见解。此外，复杂的机器学习模型可能缺乏鲁棒性，并且容易出现不稳定的行为，例如，为两个几乎相同的患者提供截然不同的风险概率。目的和目标本研究旨在开发方法，以提高根据观察数据构建的基于统计机器学习的预测模型的鲁棒性和有效性：1) 评估现有的事件时间建模统计机器学习方法的鲁棒性和稳定性，2) 开发方法 提高事件时间建模统计机器学习方法的鲁棒性和稳定性，3）使用现实世界的初级保健数据测试新方法，并将其与现有方法进行比较。研究方法的新颖性标准机器学习开发侧重于使用与准确性相关的标准来衡量预测模型的表现。然而，人们越来越意识到，在现实世界的使用中，准确性只是决定预测模型有用性的几个重要标准之一。在这项研究中，我们将研究模型训练标准的使用，其中包括考虑以下因素：(i) 模型稳定性的四个级别（如 Riley & Collins (2023) 中所定义），(ii) 更新后模型版本之间的一致性，以及 (iii) 对异常数据输入的敏感性。与 EPSRC 的战略和研究领域保持一致该项目属于“EPSRC 医疗保健技术研究领域”，其中“优化疾病” “预测、诊断和干预”是本网站列出的主题或研究领域之一 https://www.epsrc.ac.uk/research/ourportfolio/themes/它将创建用于分析大型现实世界初级保健健康数据集的新方法，支持患者特定的预测模型，并支持识别预防疾病或其复发的机会。合作该项目将涉及 与伯明翰大学合作。