Scoring systems are suboptimal for determining risk in patients with gastrointestinal
bleeding (UGIB); these might be improved by a machine learning model. We used machine
learning to develop a model to calculate risk of hospital-based intervention or death
in patients with UGIB and compared its performance with other scoring systems. We
analyzed data collected from consecutive unselected patients with UGIB from medical
centers in 4 countries (United States, Scotland, England, Denmark; n=1958) from March
2014 through March 2015. We used the data to derive and internally validate a gradient-boosting
machine learning model to identify patients who met a composite endpoint of hospital-based
intervention (transfusion or hemostatic intervention) or death within 30 days. We
compared the performance of the machine learning prediction model with validated pre-endoscopic
clinical risk scoring systems (the Glasgow-Blatchford score [GBS], admission-Rockall
score, and AIMS65). We externally validated the machine learning model using data
from 2 Asia-Pacific sites (Singapore and New Zealand; n=399). Performance was measured
by area under receiver operating characteristic curve (AUC) analysis. The machine
learning model identified patients who met the composite endpoint with an AUC of 0.91
in the in the internal validation set; the clinical scoring systems identified patients
who met the composite endpoint with AUC values of 0.88 for GBS ( P =.001), 0.73 for
Rockall score ( P <.001), and 0.78 for AIMS65 score ( P <.001). In the external validation
cohort, the machine learning model identified patients who met the composite endpoint
with an AUC of 0.90, the GBS with an AUC of 0.87 ( P =.004), the Rockall score with
an AUC of 0.66 ( P <.001), and the AIMS65 with an AUC of 0.64 ( P <.001). At cutoff
scores at which the machine learning model and GBS identified patients who met the
composite endpoint with 100% sensitivity, the specificity values were 26% with the
machine learning model vs 12% with GBS ( P <.001). We developed a machine learning
model that identifies patients with UGIB who met a composite endpoint of hospital-based
intervention or death within 30 days with a greater AUC and higher levels of specificity,
at 100% sensitivity, than validated clinical risk scoring systems. This model could
increase identification of low-risk patients who can be safely discharged from the
emergency department for outpatient management.