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Transforming Digital Phenotyping Raw Data Into Actionable Biomarkers, Quality Metrics, and Data Visualizations Using

James Burns1, Kelly Chen1, Matthew Flathers1

  • 1Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.

Journal of Medical Internet Research
|August 23, 2024
PubMed
Summary

Cortex is an open-source pipeline that streamlines digital phenotyping data analysis. It helps researchers assess data quality, derive clinical features, and create visualizations for reproducible research.

Keywords:
Cortexappappsclinicaldata analysisdata processingdata setdata visualizationdigital phenotypingmental healthmethodologymindLAMPmobile phoneopen-sourcereal worldsmartphonesmartphones

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Area of Science:

  • Digital Health
  • Computational Psychiatry
  • Data Science

Background:

  • Digital phenotyping, using data from consumer devices, is increasingly vital for research.
  • Processing this data and deriving reproducible features is a significant challenge.
  • Existing tools lack comprehensive solutions for data quality, feature extraction, and visualization.

Purpose of the Study:

  • Introduce Cortex, an open-source data processing pipeline for digital phenotyping.
  • Provide a practical guide to using Cortex for data analysis, quality assessment, and visualization.
  • Demonstrate the accessibility and rigor of digital phenotyping research through a user-friendly tool.

Main Methods:

  • Developed Cortex, an open-source Python package optimized for the mindLAMP platform.
  • Illustrated data handling, quality assessment, feature derivation, and visualization techniques.
  • Included example code for creating correlation matrices and real-world clinical applications.

Main Results:

  • Cortex enables real-time data quality assessment.
  • The pipeline facilitates the derivation of replicable clinical features from digital phenotyping data.
  • Cortex supports the creation of easily shareable data visualizations.

Conclusions:

  • Cortex enhances the accessibility and methodological rigor of digital phenotyping research.
  • The tool empowers research teams to efficiently analyze diverse digital phenotyping datasets.
  • Cortex promotes reproducible research practices in the growing field of digital health.