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Related Experiment Video

Updated: May 1, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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MS Pattern Explorer: interactive visual exploration of temporal activity patterns for multiple sclerosis.

Gabriela Morgenshtern1,2, Yves Rutishauser1, Christina Haag3

  • 1Institute for Informatics, University of Zürich, 8050 Zürich, Switzerland.

Journal of the American Medical Informatics Association : JAMIA
|September 30, 2024
PubMed
Summary
This summary is machine-generated.

MS Pattern Explorer, a visual tool using machine learning, aids clinicians in analyzing fitness wearable data for multiple sclerosis (MS) patients. It simplifies complex activity signals, accelerating insights and improving understanding of MS symptoms.

Keywords:
data visualizationinteractive machine learningmultiple sclerosissensor data explorationunderstanding patient experience

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Area of Science:

  • Biomedical Informatics
  • Human-Computer Interaction
  • Data Visualization

Background:

  • Fitness wearables generate vast amounts of activity data, posing challenges for clinical interpretation.
  • Analyzing this data is crucial for understanding disease progression and patient symptomatology, particularly in conditions like multiple sclerosis (MS).
  • Existing methods often struggle with signal overload and rapid insight generation from complex sensor data.

Purpose of the Study:

  • To design and evaluate MS Pattern Explorer, a novel visual analytics tool.
  • To leverage interactive machine learning for analyzing fitness wearable data in MS patients.
  • To address challenges in managing activity signals, accelerating insight generation, and contextualizing patterns.

Main Methods:

  • User-centered design approach prioritizing clinician needs for pattern exploration and contextualization.
  • Computation of meaningful patient activity and sleep sequences using clustering and proximity search.
  • Development of an interactive visual interface with coordinated views.
  • Evaluation involving 15 participants (clinicians, data scientists, non-experts) using usability and insight generation scoring.

Main Results:

  • MS Pattern Explorer facilitates understanding of activity patterns in temporal data.
  • The tool enables rapid insight generation and contextualization of data within and between patient cohorts.
  • Consistent performance was observed across diverse participant groups.
  • Effective support for generating insights from MS patient fitness tracker data was demonstrated.

Conclusions:

  • MS Pattern Explorer effectively reduces signal overload for clinicians analyzing activity data.
  • The tool offers novel opportunities for data exploration, sense-making, and hypothesis generation in clinical research.
  • The system has broad applicability for analyzing sensor data in chronic condition studies and cohort comparisons.