Development and explainable AI-driven characterization of a prognostic model for haploidentical transplantation outcomes

Abstract We developed a novel, explainable artificial intelligence (AI)-driven prognostic model using a contemporary single-centre cohort of 668 patients undergoing haploidentical hematopoietic cell transplantation (HCT) with post-transplantation cyclophosphamide (PTCy) prophylaxis (2015–2024) to de...

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Autori principali:	Rohtesh S. Mehta, Yosra M. Aljawai, Partow Kebriaei, Kai Cao, Jun Zou, Yudith Carmazzi, Gabriela Rondon, Betul Oran, Uday Popat, Katayoun Rezvani, Richard E. Champlin, Elizabeth J. Shpall
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Nature Portfolio 2026-03-01
Serie:	npj Digital Medicine
Accesso online:	https://doi.org/10.1038/s41746-026-02377-z
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