https://www.selleckchem.com/products/cpi-613.html
Clinical risk-scoring systems are important for identifying patients with upper gastrointestinal bleeding (UGI who are at a high risk of hemodynamic instability. We developed an algorithm that predicts adverse events in patients with initially stable non-variceal UGIB using machine learning (ML). Using prospective observational registry, 1439 out of 3363 consecutive patients were enrolled. Primary outcomes included adverse events such as mortality, hypotension, and rebleeding within 7 days. Four machine learning algorithms, namely, lo
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