Winter Michael, Langguth Berthold, Schlee Winfried, Pryss Rüdiger
Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany.
Institute of Medical Data Science, University Hospital of Würzburg, Würzburg, Germany.
NPJ Digit Med. 2024 Oct 23;7(1):299. doi: 10.1038/s41746-024-01297-0.
This perspective article explores how process mining can extract clinical insights from mobile health data and complement data-driven techniques like machine learning. Despite technological advances, challenges such as selection bias and the complex dynamics of health data require advanced approaches. Process mining focuses on analyzing temporal process patterns and provides complementary insights into health condition variability. The article highlights the potential of process mining for analyzing mHealth data and beyond.
这篇观点文章探讨了过程挖掘如何从移动健康数据中提取临床见解,并补充机器学习等数据驱动技术。尽管有技术进步,但选择偏差和健康数据的复杂动态等挑战需要先进的方法。过程挖掘专注于分析时间过程模式,并提供关于健康状况变异性的补充见解。本文强调了过程挖掘在分析移动健康数据及其他方面的潜力。