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

The American Journal of Gastroenterology
Dennis L Shung, Loren Laine

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.

References

Feb 9, 2012·The American Journal of Gastroenterology·Loren Laine, Dennis M Jensen
Jan 6, 2017·BMJ : British Medical Journal·Adrian J StanleyUNKNOWN International Gastrointestinal Bleeding Consortium
Dec 29, 2017·Digestive Diseases and Sciences·Brandon A Wuerth, Don C Rockey

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Citations

Feb 25, 2021·Journal of Gastroenterology and Hepatology·Dennis L Shung

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