Predicting immune reconstitution after antiretroviral therapy in HIV/AIDS using ensemble machine learning: a real-world study.

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Title: Predicting immune reconstitution after antiretroviral therapy in HIV/AIDS using ensemble machine learning: a real-world study.
Authors: Jin J; Department of Infectious Diseases, The Eighth's Hospital of Xi'an, Xi'an, Shaanxi, China., Li T; Drug Clinical Trial Institution Office, Xi'an Chest Hospital, Xi'an, Shaanxi, China., Chen J; Department of Infectious Diseases, The Eighth's Hospital of Xi'an, Xi'an, Shaanxi, China., Ba H; Department of Infectious Diseases, The Eighth's Hospital of Xi'an, Xi'an, Shaanxi, China., Zhang Y; Department of Infectious Diseases, The Eighth's Hospital of Xi'an, Xi'an, Shaanxi, China., Li J; Department of Infectious Diseases, The Eighth's Hospital of Xi'an, Xi'an, Shaanxi, China., Yin J; Department of Infectious Diseases, The Eighth's Hospital of Xi'an, Xi'an, Shaanxi, China., Liu H; Information Management Office, Northwestern Polytechnical University, Xi'an, Shaanxi, China., Ma K; Department of Infectious Diseases, The Eighth's Hospital of Xi'an, Xi'an, Shaanxi, China.
Source: Frontiers in immunology [Front Immunol] 2026 Apr 16; Vol. 17, pp. 1805202. Date of Electronic Publication: 2026 Apr 16 (Print Publication: 2026).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Research Foundation] Country of Publication: Switzerland NLM ID: 101560960 Publication Model: eCollection Cited Medium: Internet ISSN: 1664-3224 (Electronic) Linking ISSN: 16643224 NLM ISO Abbreviation: Front Immunol Subsets: MEDLINE
Database: MEDLINE Ultimate
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ISSN:1664-3224
DOI:10.3389/fimmu.2026.1805202