Advancing care for acute gastrointestinal bleeding using artificial intelligence

Abstract

The future of gastrointestinal bleeding will include the integration of machine learning algorithms to enhance clinician risk assessment and decision making. Machine learning algorithms have shown promise in outperforming existing clinical risk scores for both upper and lower gastrointestinal bleeding but have not been validated in any prospective clinical trials. The adoption of electronic health records provides an exciting opportunity to deploy risk prediction tools in real time and also to expand the data available to train predictive models. Machine learning algorithms can be used to identify patients with acute gastrointestinal bleeding using data extracted from the electronic health record. This can lead to an automated process to find patients with symptoms of acute gastrointestinal bleeding so that risk prediction tools can be then triggered to consistently provide decision support to the physician. Neural network models can be used to provide continuous risk predictions for patients who are at higher risk, which can be used to guide triage of patients to appropriate levels of care. Finally, the future will likely include neural network‐based analysis of endoscopic stigmata of bleeding to help guide best practices for hemostasis during the endoscopic procedure. Machine learning will enhance the delivery of care at every level for patients with acute gastrointestinal bleeding through identifying very low risk patients for outpatient management, triaging high risk patients for higher levels of care, and guiding optimal intervention during endoscopy.

A Cohort Study to Compare Effects between Ulcer- and Nonulcer-Related Nonvariceal Upper Gastrointestinal Bleeding

“…ANVUGIB is mainly caused by diseases of the upper digestive tract and biliary and pancreatic diseases. Peptic ulcers, upper gastrointestinal tumor, and acute gastric mucosal lesions were the most common [21]. This study showed that the most common causes of acute nonvaricose upper gastrointestinal bleeding were peptic ulcer, acute gastric mucosal lesion, and upper gastrointestinal tumor, and other causes were esophagocardiac mucosal tear syndrome [22].…”

Management of Patients With Acute Lower Gastrointestinal Bleeding: An Updated ACG Guideline

“…A prospective study of 170 patients with LGIB comparing a variety of pre-endoscopic scoring systems demonstrated that no score had an excellent predictive ability across all important outcomes in LGIB, including severe bleeding, need for PRBC transfusion, inhospital recurrent bleeding, and need for endoscopic intervention (52). A few machine learning algorithms have been studied in small LGIB cohorts to accurately predict specific outcomes such as need for surgery and rebleeding (53,54); however, these algorithms have not been externally validated nor are they used clinically (55).…”

Nursing Value Analysis and Risk Assessment of Acute Gastrointestinal Bleeding Using Multiagent Reinforcement Learning Algorithm

“…The effectiveness of detecting higher-risk individuals is unknown, although it has lately been investigated by anticipating in-hospital death in individuals brought to the intensive care unit with severe GIB. Machine learning technologies provide some benefits over medical risk ratings, such as the capacity to enhance over time and be retrained using information that reflects local epidemiology and illness trends [ 2 ].…”