Open Access2021 update to HIV-TRePS: a highly flexible and accurate system for the prediction of treatment response from incomplete baseline information in different healthcare settings
Author(s) -
Andrew D. Revell,
Dechao Wang,
María Jesús Pérez Elías,
Robin Wood,
Dolphina Cogill,
Hugo Tempelman,
Raph L Hamers,
Peter Reiss,
Ard van Sighem,
Catherine Rehm,
Brian K. Agan,
Gerardo Alvarez-Uria,
Julio S. G. Montaner,
H. Clifford Lane,
Brendan A. Larder,
Julio S. G. Montaner,
Richard Harrigan,
Tobias Rinke de Wit,
Kim C.E. Sigaloff,
Vincent C. Marconi,
Scott A. Wegner,
Wataru Sugiura,
Maurizio Zazzi,
Rolf Kaiser,
Eugen Schuelter,
Adrian Streinu-Cercel,
Féderico García,
Túlio de Oliveira,
José M. Gatell,
Elisa de Lazzari,
Brian Gazzard,
Mark Nelson,
Anton Pozniak,
Sundhiya Mandalia,
Colette Smith,
Lı́dia Ruiz,
Bonaventura Clotet,
Schlomo Staszewski,
Carlo Torti,
Cliff Lane,
Julie Metcalf,
Stefano Vella,
Gabrielle Dettorre,
Andrew Carr,
Richard H. Norris,
Karl Hesse,
Emanuel Vlahakis,
Roos E. Barth,
Carl Morrow,
Chris Hoffmann,
Luminiţa Ene,
Gordana Dragović,
Ricardo S. Diaz,
Cecília Sucupira,
Omar Sued,
Carina César,
Juan Sierra Madero,
Pachamuthu Balakrishnan,
S. Saravanan,
Sean Emery,
David J. Cooper,
John D. Baxter,
Laura Monno,
B Clotet,
Gastón Picchio,
Marie-Pierre deBethune,
Paul Khabo,
Lotty Ledwaba
Publication year - 2021
Publication title -
the journal of antimicrobial chemotherapy/journal of antimicrobial chemotherapy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.124
H-Index - 194
eISSN - 1460-2091
pISSN - 0305-7453
DOI - 10.1093/jac/dkab078
Subject(s) - viral load , missing data , predictive modelling , regimen , statistics , baseline (sea) , human immunodeficiency virus (hiv) , medicine , classifier (uml) , computer science , artificial intelligence , mathematics , immunology , biology , fishery
With the goal of facilitating the use of HIV-TRePS to optimize therapy in settings with limited healthcare resources, we aimed to develop computational models to predict treatment responses accurately in the absence of commonly used baseline data.