Longitudinal modeling of Post-COVID-19 condition over three years: A machine learning approach using clinical, neuropsychological, and fluid markers

Walders, Julia; Wetz, Sophie; Costa, Ana Sofia; Hofmann, Anna; Schulz, Jorg B.; Reetz, Kathrin; Dadsena, Ravi

dc.contributor.author	Hofmann, Anna
dc.date.accessioned	2026-09-29T08:28:03Z
dc.date.available	2026-09-29T08:28:03Z
dc.date.issued	2026
dc.identifier.issn	2045-2322
dc.identifier.uri	http://hdl.handle.net/10900/183927
dc.language.iso	en	de_DE
dc.publisher	Berlin : Nature Portfolio	de_DE
dc.relation.uri	http://dx.doi.org/10.1038/s41598-026-37635-3	de_DE
dc.subject.ddc	500	de_DE
dc.title	Longitudinal modeling of Post-COVID-19 condition over three years: A machine learning approach using clinical, neuropsychological, and fluid markers	de_DE
dc.type	Article	de_DE
utue.quellen.id	20260422000000_00789
utue.personen.roh	Walders, Julia
utue.personen.roh	Wetz, Sophie
utue.personen.roh	Costa, Ana Sofia
utue.personen.roh	Hofmann, Anna
utue.personen.roh	Schulz, Jorg B.
utue.personen.roh	Reetz, Kathrin
utue.personen.roh	Dadsena, Ravi
dcterms.isPartOf.ZSTitelID	Scientific Reports	de_DE
dcterms.isPartOf.ZS-Issue	Article 6517	de_DE
dcterms.isPartOf.ZS-Volume	16 (1)	de_DE
utue.fakultaet	04 Medizinische Fakultät	de_DE