Predicting the risk of acute graft-versus-host-disease after transplantation is challenging due to the presence of multimodal data and continuous evolution of disease states. A dynamic probabilistic algorithm has recently been proposed to address these challenges.
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References
Zeiser, R. & Blazar, B. R. N. Engl. J. Med. 377, 2167–2179 (2017).
Tabbara, I. A., Zimmerman, K., Morgan, C. & Nahleh, Z. Arch. Intern. Med. 162, 1558–1566 (2002).
Liu, X. et al. Nat. Comput. Sci. https://doi.org/10.1038/s43588-022-00213-4 (2022).
Tang, S. et al. JCO Clin. Cancer Inform. 4, 128–135 (2020).
Arai, Y. et al. Blood Adv. 3, 3626–3634 (2019).
Tyler, J., Choi, S. W. & Tewari, M. Curr. Opin. Sys. Biol. 20, 17–25 (2020).
Hartwell, M. J. et al. JCI Insight 2, e89798 (2017).
Levine, J. E. et al. Lancet Haematol. 2, e21–e29 (2015).
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Gupta, V. Post-transplant dynamic risk prediction. Nat Comput Sci 2, 144–145 (2022). https://doi.org/10.1038/s43588-022-00220-5
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DOI: https://doi.org/10.1038/s43588-022-00220-5
