Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: May 19, 2021
Open Peer Review Period: May 19, 2021 - Jul 14, 2021
Date Accepted: Nov 11, 2021
(closed for review but you can still tweet)
The mindLAMP Platform: Digital Phenotyping to Enable Research and Clinical Use of Patient Generated Health Data
ABSTRACT
Background:
here is a growing need for the integration of patient-generated health data (PGHD) into research and clinical care to enable personalized, preventive, more interactive care. But technical and organizational challenges such as the lack of standards and easy to use tools today preclude the effective use of PGHD generated from consumer devices such as smartphones and wearables.
Objective:
To build, assess, and disseminate an actionable digital phenotyping platform.
Methods:
The LAMP Platform addresses these challenges in enhancing clinical insight through supporting research, data analysis, and implementation efforts around PGHD as an open-source solution with freely accessible and shared code. This paper outlines how we utilized mobile app and semantic web standards like HTTP2, REST, JSON and JSON Schema, TLSv1.3, AES-256, OpenAPI, HTML5, and Vega, in conjunction with patient and provider feedback to completely update a prior version of mindLAMP.
Results:
With a simplified programming interface and novel data representation that captures additional metadata, the LAMP Platform enables interoperability with existing FHIR-based healthcare systems as well as consumer wearables and services such as Apple HealthKit and Google Fit. The companion Cortex data analysis and machine learning toolkit offers robust support for artificial intelligence, behavioral feature extraction, interactive visualizations, and high-performance data processing through parallelization and vectorization techniques.
Conclusions:
The LAMP Platform incorporates feedback from patients and clinicians alongside a standards-based approach to address these needs and function across a wide range of use-cases through its customizable and flexible components. These range from simple survey-based research to international consortiums capturing multimodal data to simple
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.