ARTIFICIAL INTELLIGENCE HELPS PREDICT THE SOURCE, SEVERITY, NEED FOR URGENT ENDOSCOPY AND DISPOSITION IN PATIENTS WITH ACUTE GASTROINTESTINAL BLEEDING

Abstract

Purpose: Develop a simple model to reliably predict: a) source, b) need for urgent endoscopy, and c) disposition in patients with acute GI bleed. Methods: Modern machine learning methods, such as artificial neural networks (ANN) and support vector machines (SVM) with learning capabilities analogous to human learning have been utilized to predict clinical outcomes. Training these models allows classification functions that generalize for all possible inputs, which can then be utilized to predict output for any given input. Relevant clinical data was collected on 117 patients representing acute upper and lower GIB. Endoscopic data was utilized to confirm the source and to ascertain if the patient would have benefitted from an urgent endoscopy. Criteria for an urgent endoscopy were: a) active & fresh bleeding, b) findings of high risk stigmata on upper endoscopy, and c) history of cirrhosis. Both endoscopic and clinical data were utilized to ascertain disposition. Performance of ANN was compared to clinicians. CLINICAL DATA Presentation Hematemesis/Coffee Grounds Hematochezia/Melena Demographic Age Comorbidities CVD/COPD Risk of Stress Ulcer Cirrhosis ASA/NSAID use Prior history of GIB Clinical Exam BP, HR, Orthostasis NG Lavage Laboratory Data Drop of Hct Platelet count Creatinine, BUN/Cr ratio Results: We utilized the SVMTorch package and Matlab coding of a standard ANN with backpropagation to implement the classifiers. Extensive evaluation through multi-fold cross-validation was not performed due to a limited dataset. 78 randomly selected patients were used for training, and 39 patients weere used for testing. Table 2 summarizes the results for each prediction variable and for each classifier. Both models yielded similar performance; the SVM model was slightly better.Table. MISCLASSIFICATIONSConclusions: Although clinical tools may not replace experience and clinical acumen, they may play an important role to help guide therapy, standardize clinical care, improve outcomes, optimize healthcare costs, and prevent adverse complications. The application of such tools in patients with GIB needs further development and validation in prospective randomized studies. If successful, this application may serve as a model for use of artificial intelligence in a variety of conditions.