Spooner Annette, Mohammadi Gelareh, Sachdev Perminder S, Brodaty Henry, Sowmya Arcot
School of Computer Science and Engineering, UNSW Sydney, Sydney, Australia.
Faculty of Medicine and Health, Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, UNSW Sydney, Sydney, Australia.
BMC Bioinformatics. 2025 Feb 17;26(1):56. doi: 10.1186/s12859-024-06018-8.
Patient data contain a wealth of information that could aid in understanding the onset and progression of disease. However, the task of modelling clinical data, which consist of multiple heterogeneous time series of different lengths, measured at different time intervals, is a complex one. A growing body of research has applied temporal pattern mining to this problem to identify common patterns in clinical attributes over time. However, the vast majority of these algorithms use techniques that are not ideally suited to clinical data. We present an efficient and scalable framework designed specifically for temporal pattern mining of real-world clinical data. Our framework combines temporal abstraction, an extended version of the efficient pattern-growth algorithm, TPMiner, the concepts of relative risk and the odds ratio to identify interesting and high-risk patterns and multiprocessing to improve computational efficiency. A complete set of cut-off values for discretisation and interpretation of the data is provided and is applicable to studies on ageing populations in general. We name this framework Clinical Temporal Pattern Mining or C-TPM.
The framework is applied to data from two real-world studies of Alzheimer's disease (AD). The patterns discovered were predictive of AD in survival analysis models with a Concordance index of up to 0.87 and contain clinically relevant variables. A visualisation module provides a clear picture of the discovered patterns for ease of interpretability.
The framework provides an effective and scalable method of modelling multivariate, longitudinal clinical data and can identify patterns in uncommon diseases and those that progress slowly over time. It is generalisable to clinical data from other medical domains as well as non-clinical data.
患者数据包含大量有助于理解疾病发生和发展的信息。然而，对临床数据进行建模是一项复杂的任务，临床数据由多个长度不同、测量时间间隔各异的异构时间序列组成。越来越多的研究将时间模式挖掘应用于该问题，以识别临床属性随时间的常见模式。然而，这些算法绝大多数使用的技术并非理想地适用于临床数据。我们提出了一个专门为现实世界临床数据的时间模式挖掘设计的高效且可扩展的框架。我们的框架结合了时间抽象、高效模式增长算法TPMiner的扩展版本、相对风险和优势比的概念以识别有趣和高风险模式，以及多处理来提高计算效率。提供了用于数据离散化和解释的完整截止值集，并且一般适用于老龄化人群的研究。我们将这个框架命名为临床时间模式挖掘（Clinical Temporal Pattern Mining，简称C - TPM）。
该框架应用于两项关于阿尔茨海默病（AD）的现实世界研究的数据。在生存分析模型中发现的模式对AD具有预测性，一致性指数高达0.87，并且包含临床相关变量。一个可视化模块提供了所发现模式的清晰图景，便于解释。
该框架提供了一种有效且可扩展的方法来对多变量纵向临床数据进行建模，并且可以识别罕见疾病以及随时间缓慢进展的疾病中的模式。它可以推广到来自其他医学领域的临床数据以及非临床数据。
