Classification of Gastrointestinal Bleeding Data Using a Web-Based Tool and Smartphone Application

• Activity Number: 356
• Type: Contributed
• Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Adrienne Chu*+
• Companies: Suffolk County Community College
• Keywords: gastrointestinal bleeding ; class prediction ; machine learning ; smartphone application

Abstract

Acute gastrointestinal bleeding (GIB) is an increasing healthcare problem due to rising NSAID (non-steroidal anti-inflammatory drugs) use in an aging population. In the emergency room (ER), the ER physician can misdiagnose a GIB patient at least 50% of the time. Classification models can be used to assist the ER physician to diagnose GIB patients more efficiently and effectively, targeting scarce healthcare resources to those who need it the most. Currently, there have not been models developed which can predict all three sources of bleeding simultaneously (upper, middle, and lower bleeding). Eight classification models were trained and tested by performing ten repetitions of ten-fold cross validation on a 192 patient dataset. Performance was assessed by accuracy and balance of sensitivity and specificity. It was determined that the random forest model performed the best overall. A web-based tool and a smartphone application were developed for a user-friendly interface that physicians and nurses can use. This web-based tool and smartphone application will be used in future studies in the hope they or something similar can be adopted for routine use in caring for GIB patients.