Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding

Scoring systems are suboptimal for determining risk in patients with gastrointestinal bleeding (UGIB); these might be improved by a machine learning model. We used machine learning to develop a model to calculate risk of hospital-based intervention or death in patients with UGIB and compared its performance with other scoring systems.

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