Ten key considerations for developing interpretable risk prediction models
Artificial intelligence (AI) and machine learning (ML) have rapidly transformed clinical risk prediction by enabling high-dimensional data analysis, automated pattern recognition, and individualized risk estimation [1]. Compared with conventional regression models, ML algorithms can automatically learn nonlinear relati...
Xi Zhang, Wenjie Xu, David R. Smith
· Frontiers in Bioinformatics · 0 citations