MS Pattern Explorer: interactive visual exploration of temporal activity patterns for multiple sclerosis.

ABSTRACT:

Objectives

This article describes the design and evaluation of MS Pattern Explorer, a novel visual tool that uses interactive machine learning to analyze fitness wearables' data. Applied to a clinical study of multiple sclerosis (MS) patients, the tool addresses key challenges: managing activity signals, accelerating insight generation, and rapidly contextualizing identified patterns. By analyzing sensor measurements, it aims to enhance understanding of MS symptomatology and improve the broader problem of clinical exploratory sensor data analysis.

Materials and methods

Following a user-centered design approach, we learned that clinicians have 3 priorities for generating insights for the Barka-MS study data: exploration and search for, and contextualization of, sequences and

SUBMITTER: Morgenshtern G 

PROVIDER: S-EPMC11491606 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature