Process mining in mHealth data analysis

Michael Winter; Berthold Langguth; Winfried Schlee; Rüdiger Pryss

doi:10.1038/s41746-024-01297-0

Process mining in mHealth data analysis

NPJ Digit Med. 2024 Oct 23;7(1):299. doi: 10.1038/s41746-024-01297-0.

Authors

Michael Winter^{1

2}, Berthold Langguth³, Winfried Schlee^#^{3

4}, Rüdiger Pryss^#^{5

6}

Affiliations

¹ Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany. [email protected].
² Institute of Medical Data Science, University Hospital of Würzburg, Würzburg, Germany. [email protected].
³ Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany.
⁴ Eastern Switzerland University of Applied Sciences, St. Gallen, Switzerland.
⁵ Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany.
⁶ Institute of Medical Data Science, University Hospital of Würzburg, Würzburg, Germany.

^# Contributed equally.

Abstract

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.

Publication types

Review