{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Jayavelu ND"],"funding":["NIAID NIH HHS","NIH HHS"],"pubmed_abstract":["The post-acute sequelae of SARS-CoV-2 (PASC), also known as long COVID, remain a significant health issue that is incompletely understood. Predicting which acutely infected individuals will go on to develop long COVID is challenging due to the lack of established biomarkers, clear disease mechanisms, or well-defined sub-phenotypes. Machine learning (ML) models offer the potential to address this by leveraging clinical data to enhance diagnostic precision. We utilized clinical data, including antibody titers and viral load measurements collected at the time of hospital admission, to predict the likelihood of acute COVID-19 progressing to long COVID. Our machine learning models achieved median AUROC values ranging from 0.64 to 0.66 and AUPRC values between 0.51 and 0.54, demonstrating their "],"journal":["medRxiv : the preprint server for health sciences"],"pagination":["2025.02.12.25322164"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11844586"],"repository":["biostudies-literature"],"pubmed_title":["Machine learning models predict long COVID outcomes based on baseline clinical and immunologic factors."],"pmcid":["PMC11844586"],"funding_grant_id":["R01 AI135803","R01 AI104870","R01 AI132774","P51 OD011132","U19 AI062629","U19 AI090023","U54 AI142766","U19 AI118610","U19 AI077439","R01 AI145835","U19 AI118608","U19 AI125357","U19 AI057229","U19 AI128910","U19 AI089992","U19 AI128913","S10 OD026799"],"pubmed_authors":["Peters B","Higuita NIA","Simon V","Langelier CR","Milliren CE","Kheradmand F","Schaenman J","Rouphael N","Maecker HT","McComsey GA","Sekaly RP","Corry DB","Davis MM","Reed EF","Metcalf JP","Ehrlich LIR","Haddad EK","Diray-Arce J","Ozonoff A","Samaha H","Melamed E","Cairns CB","Baden LR","Brakenridge SC","Pulendran B","Hough CL","Nadeau KC","Montgomery RR","Kraft M","Bime C","Levy O","IMPACC Network","Gygi JP","Wimalasena ST","Hoch A","Hafler DA","Liu S","Sesma AF","Calfee CS","Krammer F","Gabernet G","Erle DJ","Kleinstein SH","Shaw AC","Altman MC","Messer WB","Guan L","Jayavelu ND","Atkinson MA","Geng LN"],"additional_accession":[]},"is_claimable":false,"name":"Machine learning models predict long COVID outcomes based on baseline clinical and immunologic factors.","description":"The post-acute sequelae of SARS-CoV-2 (PASC), also known as long COVID, remain a significant health issue that is incompletely understood. Predicting which acutely infected individuals will go on to develop long COVID is challenging due to the lack of established biomarkers, clear disease mechanisms, or well-defined sub-phenotypes. Machine learning (ML) models offer the potential to address this by leveraging clinical data to enhance diagnostic precision. We utilized clinical data, including antibody titers and viral load measurements collected at the time of hospital admission, to predict the likelihood of acute COVID-19 progressing to long COVID. Our machine learning models achieved median AUROC values ranging from 0.64 to 0.66 and AUPRC values between 0.51 and 0.54, demonstrating their ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Feb","modification":"2026-07-09T12:22:29.773Z","creation":"2025-04-04T01:28:04.495Z"},"accession":"S-EPMC11844586","cross_references":{"pubmed":["39990570"],"doi":["10.1101/2025.02.12.25322164"]}}