ArticleScientific reports2024
Long COVID diagnostic with differentiation from chronic lyme disease using machine learning and cytokine hubs.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- COVID-19 and Lung Cancer Interactions: A Literature Review.Medical sciences (Basel, Switzerland) · 2025Review
- Perioperative and anesthetic considerations for post-acute sequelae of COVID (PASC)/long COVID.Perioperative medicine (London, England) · 2025Review
- External Validation of Raman Spectroscopy for Lyme Disease Diagnostics.Journal of biophotonics · 2025Article
- Core features and inherent diversity of post-acute infection syndromes.Frontiers in immunology · 2025Review
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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