Machine Learning Prognostic Models for Gastrointestinal Bleeding Using Electronic Health Record Data

Abstract

Risk assessment tools for patients with gastrointestinal bleeding may be used for determining level of care and informing management decisions. Development of models that use data from electronic health records is an important step for future deployment of such tools in clinical practice. Furthermore, machine learning tools have the potential to outperform standard clinical risk assessment tools. The authors developed a new machine learning tool for the outcome of in-hospital mortality and suggested it outperforms the intensive care unit prognostic tool, APACHE IVa. Limitations include lack of generalizability beyond intensive care unit patients, inability to use early in the hospital course, and lack of external validation.