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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Mar 17, 2024
Date Accepted: Jun 19, 2024
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Transforming Digital Phenotyping Raw Data Into Actionable Biomarkers, Quality Metrics, and Data Visualizations Using Cortex Software Package: Tutorial

Burns J, Chen K, Flathers M, Currey D, Macrynikola N, Vaidyam A, Langholm C, Barnett I, Byun A(S, Lane E, Torous J

Transforming Digital Phenotyping Raw Data Into Actionable Biomarkers, Quality Metrics, and Data Visualizations Using Cortex Software Package: Tutorial

J Med Internet Res 2024;26:e58502

DOI: 10.2196/58502

PMID: 39178032

PMCID: 11380059

Transforming Digital Phenotyping Raw Data into Actionable Biomarkers, Quality Metrics, and Data Visualizations: An Introduction to the Cortex Software Package

  • James Burns; 
  • Kelly Chen; 
  • Matthew Flathers; 
  • Danielle Currey; 
  • Natalia Macrynikola; 
  • Aditya Vaidyam; 
  • Carsten Langholm; 
  • Ian Barnett; 
  • Andrew (Jin Soo) Byun; 
  • Erlend Lane; 
  • John Torous

ABSTRACT

As digital phenotyping, the capture of active and passive data from consumer devices like smartphones, becomes more common the need to properly process the data and derive replicable features from it has become paramount. Cortex is an open-source data processing pipeline for digital phenotyping data, optimized for use with the mindLAMP apps which is used by nearly 100 research teams across the world. Cortex is designed to help teams 1) assess digital phenotyping data quality in real time, 2) derive replicable clinical features from the data, 3) and enable easy to share data visualizations. Cortex offers many options to work with digital phenotyping data, although some common approaches are likely of value to all teams using it. This paper highlights the reasoning, code, and example steps necessary to fully work with digital phenotyping data in an streamlined manner. Covering how to work with the data, assess its quality, derives features, and visualize findings, this paper is designed to offer the reader the knowledge and skills to apply towards analyzing any digital phenotyping dataset. Towards highlighting clinical applications, this paper also provides three easy to implement examples of Cortex use in real world settings. Through understanding how to work with digital phenotyping data and providing ready to deploy code with Cortex, the paper aims to show the new field of digital phenotyping can be both accessible to all yet still rigorous in methodology.


 Citation

Please cite as:

Burns J, Chen K, Flathers M, Currey D, Macrynikola N, Vaidyam A, Langholm C, Barnett I, Byun A(S, Lane E, Torous J

Transforming Digital Phenotyping Raw Data Into Actionable Biomarkers, Quality Metrics, and Data Visualizations Using Cortex Software Package: Tutorial

J Med Internet Res 2024;26:e58502

DOI: 10.2196/58502

PMID: 39178032

PMCID: 11380059

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