Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire.
Brief Bioinform
; 24(1)2023 01 19.
Article
in English
| MEDLINE | ID: covidwho-2188252
ABSTRACT
Underlying medical conditions, such as cancer, kidney disease and heart failure, are associated with a higher risk for severe COVID-19. Accurate classification of COVID-19 patients with underlying medical conditions is critical for personalized treatment decision and prognosis estimation. In this study, we propose an interpretable artificial intelligence model termed VDJMiner to mine the underlying medical conditions and predict the prognosis of COVID-19 patients according to their immune repertoires. In a cohort of more than 1400 COVID-19 patients, VDJMiner accurately identifies multiple underlying medical conditions, including cancers, chronic kidney disease, autoimmune disease, diabetes, congestive heart failure, coronary artery disease, asthma and chronic obstructive pulmonary disease, with an average area under the receiver operating characteristic curve (AUC) of 0.961. Meanwhile, in this same cohort, VDJMiner achieves an AUC of 0.922 in predicting severe COVID-19. Moreover, VDJMiner achieves an accuracy of 0.857 in predicting the response of COVID-19 patients to tocilizumab treatment on the leave-one-out test. Additionally, VDJMiner interpretively mines and scores V(D)J gene segments of the T-cell receptors that are associated with the disease. The identified associations between single-cell V(D)J gene segments and COVID-19 are highly consistent with previous studies. The source code of VDJMiner is publicly accessible at https//github.com/TencentAILabHealthcare/VDJMiner. The web server of VDJMiner is available at https//gene.ai.tencent.com/VDJMiner/.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Asthma
/
COVID-19
Type of study:
Cohort study
/
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal subject:
Biology
/
Medical Informatics
Year:
2023
Document Type:
Article
Affiliation country:
Bib
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