<u>A</u> I- <u>T</u> echniques <u>L</u> oss-Based <u>A</u> lgorithm for <u>S</u> everity Classification (ATLAS): a novel approach for continuous quantification of exertional symptoms during incremental exercise testing
We used innovative informatics research to change paradigms in displaying, quantifying, and analyzing effort-related symptoms in patient populations.
Authors
- Danilo Cortozi Berton (ORCID: https://orcid.org/0000-0002-8393-3126)
- Sophia Wang (ORCID: https://orcid.org/0000-0003-2597-9393)
- Matthew D. James (ORCID: https://orcid.org/0000-0003-1586-6897)
- Nicolle Jasmin Domnik (ORCID: https://orcid.org/0000-0001-6883-4720)
- Denis E. O’Donnell (ORCID: https://orcid.org/0000-0001-7593-2433)
- José Alberto Neder (ORCID: https://orcid.org/0000-0002-8019-281X)
- Devin B. Phillips (ORCID: https://orcid.org/0000-0002-0664-5171)
- Igor Neder‐Serafini
- Luíz Eduardo Nery (ORCID: https://orcid.org/0000-0003-3636-057X)
- Sandra G. Vincent
- Amin Abu Hijleh (ORCID: https://orcid.org/0009-0000-1012-0055)
Institutions
- Universidade Federal do Rio Grande do Sul (BR)
- York University (CA)
- Queen's University (CA)
- Universidade Federal de São Paulo (BR)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-03-11
- DOI
- https://doi.org/10.1093/jamia/ocaf051
- Citations
- 3
- Primary Topic
- Sports Performance and Training
- Type
- article
- Field-Weighted Citation Impact
- 5.16
Funders
- Queen's University
- Southeastern Ontario Academic Medical Organization