1. Long COVID diagnostic with differentiation from chronic lyme disease using machine learning and cytokine hubs.
- Author
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Patterson, Bruce K., Guevara-Coto, Jose, Mora, Javier, Francisco, Edgar B., Yogendra, Ram, Mora-Rodríguez, Rodrigo A., Beaty, Christopher, Lemaster, Gwyneth, Kaplan DO, Gary, Katz, Amiram, and Bellanti, Joseph A.
- Subjects
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POST-acute COVID-19 syndrome , *LYME disease , *CHRONIC diseases , *CYTOKINES , *RANDOM forest algorithms - Abstract
The absence of a long COVID (LC) or post-acute sequelae of COVID-19 (PASC) diagnostic has profound implications for research and potential therapeutics given the lack of specificity with symptom-based identification of LC and the overlap of symptoms with other chronic inflammatory conditions. Here, we report a machine-learning approach to LC/PASC diagnosis on 347 individuals using cytokine hubs that are also capable of differentiating LC from chronic lyme disease (CLD). We derived decision tree, random forest, and gradient-boosting machine (GBM) classifiers and compared their diagnostic capabilities on a dataset partitioned into training (178 individuals) and evaluation (45 individuals) sets. The GBM model generated 89% sensitivity and 96% specificity for LC with no evidence of overfitting. We tested the GBM on an additional random dataset (106 LC/PASC and 18 Lyme), resulting in high sensitivity (97%) and specificity (90%) for LC. We constructed a Lyme Index confirmatory algorithm to discriminate LC and CLD. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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