A Machine Learning Approach to Determine Risk Factors for Respiratory Bacterial/Fungal Coinfection in Critically Ill Patients with Influenza and SARS-CoV-2 Infection: A Spanish Perspective

• Main Authors: Alejandro Rodríguez (Author), Josep Gómez (Author), Ignacio Martín-Loeches (Author), Laura Claverias (Author), Emili Díaz (Author), Rafael Zaragoza (Author), Marcio Borges-Sa (Author), Frederic Gómez-Bertomeu (Author), Álvaro Franquet (Author), Sandra Trefler (Author), Carlos González Garzón (Author), Lissett Cortés (Author), Florencia Alés (Author), Susana Sancho (Author), Jordi Solé-Violán (Author), Ángel Estella (Author), Julen Berrueta (Author), Alejandro García-Martínez (Author), Borja Suberviola (Author), Juan J. Guardiola (Author), María Bodí (Author)
• Format: Book
• Published: MDPI AG, 2024-10-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

<b>Background</b>: Bacterial/fungal coinfections (COIs) are associated with antibiotic overuse, poor outcomes such as prolonged ICU stay, and increased mortality. Our aim was to develop machine learning-based predictive models to identify respiratory bacterial or fungal coinfections upon...