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Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable

Ahnjili ZhuParris1,2,3, Annika A de Goede1, Iris E Yocarini2

  • 1Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands.

Sensors (Basel, Switzerland)
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PubMed
Summary

Mobile health (mHealth) and Machine Learning (ML) create powerful biomarkers for monitoring central nervous system (CNS) disorders remotely. Further research is needed to standardize methods for improved clinical application.

Keywords:
biomarkercentral nervous systemclinical trialsmHealthmachine learningremote monitoringsmartphoneswearables

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Area of Science:

  • Biomedical Engineering
  • Digital Health
  • Computational Neuroscience

Background:

  • Central nervous system (CNS) disorders require continuous monitoring for disease progression and treatment evaluation.
  • Mobile health (mHealth) technologies enable remote, continuous patient symptom monitoring.
  • Machine Learning (ML) can transform mHealth data into precise biomarkers of disease activity.

Purpose of the Study:

  • To review the current state of biomarker development using mHealth and ML.
  • To propose recommendations for ensuring biomarker accuracy, reliability, and interpretability.

Main Methods:

  • Literature search across PubMed, IEEE, and CTTI databases.
  • Extraction and review of ML methods used in selected publications.

Main Results:

  • Synthesis of 66 publications on mHealth-based biomarker development using ML.
  • Identification of approaches and recommendations for creating representative, reproducible, and interpretable biomarkers.

Conclusions:

  • mHealth and ML-derived biomarkers show significant potential for remote CNS disorder monitoring.
  • Standardization of study designs and further research are crucial for advancing the field.
  • Continued innovation in mHealth biomarkers can enhance CNS disorder management.