Deep Learning Approaches to Risk Stratification in Acute Gastrointestinal Bleeding

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PROJECT SUMMARY Acute gastrointestinal bleeding accounts for over 2.2 million hospital days and 19.2 billion dollars of medical charges annually. 52% to 55% of patients with acute gastrointestinal bleeding are unnecessarily hospitalized, leading to wasted resources. Although risk stratification of patients presenting with gastrointestinal bleeding is recommended, risk-assessment scoring systems are not commonly used in practice, have sub- optimal performance, may be applied incorrectly, and are not easily updated. Most current risk scores were designed for use based on the location of the bleeding source: upper or lower gastrointestinal tract. However, the location of the bleeding source is not always clear at presentation. A risk score that bases initial assessment on presenting symptoms (e.g., hematemesis, melena, hematochezia) is more relevant and useful in clinical practice. The electronic health record can be used to identify patients with acute gastrointestinal bleeding symptoms and extract clinical data to automatically calculate risk scores that are made available to providers. Machine learning (field of study that gives computers the ability to learn without being explicitly programmed), particularly deep learning using neural networks (collection of nodes that process and transmit information), can create electronic health record-based models that perform better than clinical risk scores for gastrointestinal bleeding and are well-suited for learning from new data. This proposal will use deep learning tools on electronic health record data to decrease unnecessary hospitalization in patients with acute gastrointestinal bleeding by identifying low risk patients. The goals are to 1) Develop and validate an accurate and clinically useful deep learning algorithm for initial risk stratification superior to existing clinical risk scores 2) Develop and validate a dynamic deep learning tool to model risk over time, and 3) Pilot the best performing algorithms in the electronic health record. Deep learning algorithms will be developed using a dataset of electronic health record data of 7,000 patients with acute gastrointestinal bleeding from two academic hospitals in the Yale-New Haven Health System. Validation will be performed on a separate dataset of patients at Partners Healthcare in Boston, Massachusetts. Neural network approaches will be applied to patients’ data updated over time to evaluate the trajectory towards requiring transfusion of red blood cells. Finally, a pilot study will implement the best-performing algorithms in the electronic health record for a 3-month period to test feasibility of deployment and acceptability to providers and patients. Planned coursework includes deep learning with biomedical data, risk assessment and longitudinal analysis. This work has potential to generate cost savings through better integrated risk stratification of patients presenting with overt gastrointestinal bleeding. To meet the research and educational goals of this proposal, the mentorship team includes a primary mentor in gastrointestinal bleeding and co-mentors in deep learning, electronic health record-based clinical trial design of prognostic algorithms, and implementation science.