Developing a Multisensor-Based Machine Learning Technology (Aidar Decompensation Index) for Real-Time Automated Detection of Post–COVID-19 Condition: Protocol for an Observational Study

Authors

Jenny Mathew
Varsha G. Vimalananda (ORCID: https://orcid.org/0000-0003-0738-672X)
Jaclyn Pagliaro (ORCID: https://orcid.org/0000-0003-4289-3073)
Lauren K. Wash (ORCID: https://orcid.org/0000-0002-9279-6602)
Sathyanarayanan Elumalai
Ka Ly
Alison Leibowitz

Institutions

Boston University (US)
Providence VA Medical Center (US)
VA New England Healthcare System (US)
Evidation Health (United States) (US)

Publication Details

Journal: JMIR Research Protocols
Published: 2025-02-22
DOI: https://doi.org/10.2196/54993
Citations: 1
Primary Topic: Long-Term Effects of COVID-19
Type: article
Field-Weighted Citation Impact: 1.39

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article

Developing a Multisensor-Based Machine Learning Technology (Aidar Decompensation Index) for Real-Time Automated Detection of Post–COVID-19 Condition: Protocol for an Observational Study

Jenny Mathew, Varsha G. Vimalananda, Jaclyn Pagliaro, Lauren K. Wash et al.

Feb 22, 2025

1 citations

JMIR Research Protocols

Long-Term Effects of COVID-19

1.39

article

Developing a Multisensor-Based Machine Learning Technology (Aidar Decompensation Index) for Real-Time Automated Detection of Post–COVID-19 Condition: Protocol for an Observational Study

Jenny Mathew, Varsha G. Vimalananda, Jaclyn Pagliaro, Lauren K. Wash, Sathyanarayanan Elumalai, Ka Ly, Alison Leibowitz

February 22, 2025article en

1 citations

Abstract

No abstract available for this paper.

JMIR Research ProtocolsVol. 14

Boston University (US), Providence VA Medical Center (US), VA New England Healthcare System (US), Evidation Health (United States) (US)

Industry, innovation and infrastructure

Openalex Percentile: Top 23%

Long-Term Effects of COVID-19

1.39

DOI Open Access PDF OpenAlex

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