Long COVID diagnostic with differentiation from chronic lyme disease using machine learning and cytokine hubs
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| Autori: | , , , , , , , , , , |
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| Natura: | artículo original |
| Data di pubblicazione: | 2024 |
| Descrizione: | 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. |
| Stato: | Kérwá |
| Istituzione: | Universidad de Costa Rica |
| Repositorio: | Kérwá |
| Lingua: | Inglés |
| OAI Identifier: | oai:kerwa.ucr.ac.cr:10669/102843 |
| Accesso online: | https://hdl.handle.net/10669/102843 https://doi.org/10.1038/s41598-024-70929-y |
| Keyword: | COVID-19 post-acute sequelae of COVID-19 long COVID cytokines chronic lyme disease myalgic encephalomyelitis-chronic fatigue syndrome machine learning/AI |