A machine learning-based risk prediction tool for early identification of invasive pulmonary aspergillosis in immunocompetent patients: a systematic review and meta-analysis

Key Points

• Accurate risk stratification for invasive pulmonary aspergillosis is achieved using a machine learning tool.
• This tool demonstrates a strong prediction accuracy with a robust design, integrating clinical data with meta-evidence.
• The approach captures complex interactions between predictors, showcasing its ability for nonlinear analyses.
• Further validation in diverse clinical settings may enhance its applicability and impact on treatment outcomes.

Abstract