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

• Asıl Yazarlar: Alejandro Rodríguez (Yazar), Josep Gómez (Yazar), Ignacio Martín-Loeches (Yazar), Laura Claverias (Yazar), Emili Díaz (Yazar), Rafael Zaragoza (Yazar), Marcio Borges-Sa (Yazar), Frederic Gómez-Bertomeu (Yazar), Álvaro Franquet (Yazar), Sandra Trefler (Yazar), Carlos González Garzón (Yazar), Lissett Cortés (Yazar), Florencia Alés (Yazar), Susana Sancho (Yazar), Jordi Solé-Violán (Yazar), Ángel Estella (Yazar), Julen Berrueta (Yazar), Alejandro García-Martínez (Yazar), Borja Suberviola (Yazar), Juan J. Guardiola (Yazar), María Bodí (Yazar)
• Materyal Türü: Kitap
• Baskı/Yayın Bilgisi: MDPI AG, 2024-10-01T00:00:00Z.
• Konular: article
• Online Erişim: 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...