Csalódi Róbert, Bagyura Zsolt, Abonyi János
HUN-REN-PE Complex Systems Monitoring Research Group, University of Pannonia, Egyetem str. 10, POB 158, Veszprém H-8200, Hungary.
Department of Process Engineering, University of Pannonia, Egyetem str. 10, POB 158, Veszprém H-8200, Hungary.
MethodsX. 2023 Dec 29;12:102535. doi: 10.1016/j.mex.2023.102535. eCollection 2024 Jun.
The analysis of event sequences with temporal dependencies holds substantial importance across various domains, including healthcare. This study introduces a novel approach that combines sequential rule mining and survival analysis to uncover significant associations and temporal patterns within event sequences. By integrating these techniques, we address the limitations linked to the loss of temporal information. The methodology extends traditional sequential rule mining by introducing time-dependent confidence functions, providing a comprehensive understanding of relationships between antecedent and consequent events. The incorporation of the Kaplan-Meier estimator of survival analysis enables the calculation of temporal distributions between events, resulting in time-dependent confidence functions. These confidence functions illuminate the probability of specific event occurrences considering temporal contexts. To present the application of the method, we demonstrated the usage within the healthcare domain. Analyzing the ICD-10 codes and the laboratory events, we successfully identified relevant sequential rules and their time-dependent confidence functions. This empirical validation underscores the potential of methodology to uncover clinically significant associations within intricate medical data.•The study presents a unique methodology that integrates sequential rule mining and survival analysis.•The methodology extends traditional sequential rule mining by introducing time-dependent confidence functions.•The application of the method is demonstrated within the healthcare domain.
对具有时间依赖性的事件序列进行分析在包括医疗保健在内的各个领域都具有重要意义。本研究引入了一种新颖的方法，该方法结合了序列规则挖掘和生存分析，以揭示事件序列中的重要关联和时间模式。通过整合这些技术，我们解决了与时间信息丢失相关的局限性。该方法通过引入与时间相关的置信函数扩展了传统的序列规则挖掘，从而全面理解先行事件和后续事件之间的关系。生存分析中Kaplan-Meier估计器的纳入使得能够计算事件之间的时间分布，从而产生与时间相关的置信函数。这些置信函数阐明了在考虑时间背景的情况下特定事件发生的概率。为了展示该方法的应用，我们在医疗保健领域进行了演示。通过分析ICD-10编码和实验室事件，我们成功识别了相关的序列规则及其与时间相关的置信函数。这一实证验证强调了该方法在复杂医疗数据中揭示具有临床意义的关联的潜力。
•本研究提出了一种独特的方法，该方法整合了序列规则挖掘和生存分析。
•该方法通过引入与时间相关的置信函数扩展了传统的序列规则挖掘。
•该方法在医疗保健领域进行了演示。
