Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire

• Published in: Briefings in bioinformatics Vol. 24; no. 1
• Main Authors: Zhao, Yu, He, Bing, Xu, Zhimeng, Zhang, Yidan, Zhao, Xuan, Huang, Zhi-An, Yang, Fan, Wang, Liang, Duan, Lei, Song, Jiangning, Yao, Jianhua
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 19.01.2023; Oxford Publishing Limited (England)
• Subjects: Artificial Intelligence; Asthma; Autoimmune diseases; Cancer; Cardiovascular disease; Chronic obstructive pulmonary disease; Congestive heart failure; Coronary artery disease; COVID-19; Diabetes mellitus; Health services; Heart failure; Humans; J gene; Kidney diseases; Kidneys; Lung diseases; Lymphocytes T; Monoclonal antibodies; Patients; Prognosis; Pulmonary artery; Renal failure; ROC Curve; Segments; Software; Source code; COVID-19; diagnosis; prognosis; artificial intelligence; TCR repertoire

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. I...