Process Mining in MHealth Data Analysis

Overview

Journal NPJ Digit Med

Specialty Medical Informatics

Date 2024 Oct 24

PMID 39443677

Authors

Michael Winter

Berthold Langguth

Winfried Schlee

Rudiger Pryss

Affiliations

Soon will be listed here.

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.

Citing Articles

Healthcare trajectories of aging individuals during their last year of life: application of process mining methods to administrative health databases.

Bosson-Rieutort D, Langford-Avelar A, Duc J, Dalmas B BMC Med Inform Decis Mak. 2025; 25(1):58.

PMID: 39910601 PMC: 11796206. DOI: 10.1186/s12911-025-02898-9.

References

van der Wal A, Luyten T, Cardon E, Jacquemin L, Vanderveken O, Topsakal V . Sex Differences in the Response to Different Tinnitus Treatment. Front Neurosci. 2020; 14:422. PMC: 7235341. DOI: 10.3389/fnins.2020.00422. View

Rojas E, Munoz-Gama J, Sepulveda M, Capurro D . Process mining in healthcare: A literature review. J Biomed Inform. 2016; 61:224-36. DOI: 10.1016/j.jbi.2016.04.007. View

Cheung Y, Sabrina Hsueh P, Qian M, Yoon S, Meli L, Diaz K . Are Nomothetic or Ideographic Approaches Superior in Predicting Daily Exercise Behaviors?. Methods Inf Med. 2018; 56(6):452-460. PMC: 5897129. DOI: 10.3414/ME16-02-0051. View

De Roock E, Martin N . Process mining in healthcare - An updated perspective on the state of the art. J Biomed Inform. 2022; 127:103995. DOI: 10.1016/j.jbi.2022.103995. View

Janiaud P, Serghiou S, Ioannidis J . New clinical trial designs in the era of precision medicine: An overview of definitions, strengths, weaknesses, and current use in oncology. Cancer Treat Rev. 2018; 73:20-30. DOI: 10.1016/j.ctrv.2018.12.003. View

Kluzek S, Dean B, Wartolowska K . Patient-reported outcome measures (PROMs) as proof of treatment efficacy. BMJ Evid Based Med. 2021; 27(3):153-155. PMC: 9132863. DOI: 10.1136/bmjebm-2020-111573. View

Beltz A, Wright A, Sprague B, Molenaar P . Bridging the Nomothetic and Idiographic Approaches to the Analysis of Clinical Data. Assessment. 2016; 23(4):447-458. PMC: 5104664. DOI: 10.1177/1073191116648209. View

Basch E, Deal A, Dueck A, Scher H, Kris M, Hudis C . Overall Survival Results of a Trial Assessing Patient-Reported Outcomes for Symptom Monitoring During Routine Cancer Treatment. JAMA. 2017; 318(2):197-198. PMC: 5817466. DOI: 10.1001/jama.2017.7156. View

Lohr K, Zebrack B . Using patient-reported outcomes in clinical practice: challenges and opportunities. Qual Life Res. 2008; 18(1):99-107. DOI: 10.1007/s11136-008-9413-7. View

10.

Schuster D, Benevento E, Aloini D, van der Aalst W . Analyzing Healthcare Processes with Incremental Process Discovery: Practical Insights from a Real-World Application. J Healthc Inform Res. 2024; 8(3):523-554. PMC: 11310182. DOI: 10.1007/s41666-024-00165-6. View

11.

Greenberg J, Frumkin M, Xu Z, Zhang J, Javeed S, Zhang J . Preoperative Mobile Health Data Improve Predictions of Recovery From Lumbar Spine Surgery. Neurosurgery. 2024; 95(3):617-626. DOI: 10.1227/neu.0000000000002911. View

12.

Intille S, Haynes C, Maniar D, Ponnada A, Manjourides J . μEMA: Microinteraction-based Ecological Momentary Assessment (EMA) Using a Smartwatch. Proc ACM Int Conf Ubiquitous Comput. 2018; 2016:1124-1128. PMC: 6143290. DOI: 10.1145/2971648.2971717. View

13.

Fisher A . Toward a dynamic model of psychological assessment: Implications for personalized care. J Consult Clin Psychol. 2015; 83(4):825-36. DOI: 10.1037/ccp0000026. View

14.

Torous J, Bucci S, Bell I, Kessing L, Faurholt-Jepsen M, Whelan P . The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry. 2021; 20(3):318-335. PMC: 8429349. DOI: 10.1002/wps.20883. View

15.

Allgaier J, Pryss R . Practical approaches in evaluating validation and biases of machine learning applied to mobile health studies. Commun Med (Lond). 2024; 4(1):76. PMC: 11035658. DOI: 10.1038/s43856-024-00468-0. View

16.

Moskowitz D, Young S . Ecological momentary assessment: what it is and why it is a method of the future in clinical psychopharmacology. J Psychiatry Neurosci. 2006; 31(1):13-20. PMC: 1325062. View

17.

Kraft R, Schlee W, Stach M, Reichert M, Langguth B, Baumeister H . Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain. Front Neurosci. 2020; 14:164. PMC: 7058696. DOI: 10.3389/fnins.2020.00164. View

18.

Saturni S, Bellini F, Braido F, Paggiaro P, Sanduzzi A, Scichilone N . Randomized Controlled Trials and real life studies. Approaches and methodologies: a clinical point of view. Pulm Pharmacol Ther. 2014; 27(2):129-38. DOI: 10.1016/j.pupt.2014.01.005. View

19.

Dahlin S, Eriksson H, Raharjo H . Process Mining for Quality Improvement: Propositions for Practice and Research. Qual Manag Health Care. 2018; 28(1):8-14. DOI: 10.1097/QMH.0000000000000197. View

20.

Munoz-Gama J, Martin N, Fernandez-Llatas C, Johnson O, Sepulveda M, Helm E . Process mining for healthcare: Characteristics and challenges. J Biomed Inform. 2022; 127:103994. DOI: 10.1016/j.jbi.2022.103994. View