A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolism

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A machine learning approach to identify patients at risk for long-term consequences after pulmonary embolism
  • S.N. and C.S. contributed to the study’s conceptualization, design, and data analysis and drafted the initial manuscript. C.A. and M.M. supervised the study, providing critical insights and guidance. B.B., A.A.-C., L.H.-B., M.L.P., R.O., and D.J. contributed to manuscript revision, providing critical feedback and important intellectual content.

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