Charat Thongprayoon, Pradeep Vaitla, Voravech Nissaisorakarn, Michael A Mao, Jose L Zabala Genovez, Andrea G Kattah, Pattharawin Pattharanitima, Saraschandra Vallabhajosyula, Mira T Keddis, Fawad Qureshi, John J Dillon, Vesna D Garovic, Kianoush B Kashani, Wisit Cheungpasitporn
BACKGROUND: We aimed to cluster patients with acute kidney injury at hospital admission into clinically distinct subtypes using an unsupervised machine learning approach and assess the mortality risk among the distinct clusters. METHODS: We performed consensus clustering analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 4289 hospitalized adult patients with acute kidney injury at admission. The standardized difference of each variable was calculated to identify each cluster's key features...
September 24, 2021: Medical Sciences: Open Access Journal