Predictive survival modelings for HIV-related cryptococcosis: comparing machine learning approaches.

Journal: Frontiers in cellular and infection microbiology
Published Date: Jan 1, 2025

Abstract

INTRODUCTION: HIV-associated cryptococcosis is marked by unpredictable disease trajectories and persistently high mortality rates worldwide. Although improved risk stratification and tailored clinical management are urgently needed to enhance patient survival, such strategies remain limited.

Authors

  • Xuemin Fu
    Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany.
  • Luling Wu
    Department of Chemistry, University of Bath, Bath, BA2 7AY, UK.
  • Jingna Xun
    Department of Infectious Diseases and Immunology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
  • Benno Pütz
    Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany.
  • Zhihang Zheng
    National Clinical Research Center for Infectious Disease, Shenzhen Third Peoples Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China.
  • Yanpeng Li
    Mayo Clinic, Rochester, MN, USA.
  • Yinzhong Shen
    Department of Infectious Diseases and Immunology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
  • Hongzhou Lu
  • Jun Chen
    Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA 90095, USA.
  • Bertram Müller-Myhsok
    Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.

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