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Deep Learning Approaches to Risk Stratification in Acute Gastrointestinal Bleeding

急性胃肠出血风险分层的深度学习方法

基本信息

批准号：
10696199
负责人：
Dennis Shung
金额：
$ 19.39万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2026-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10696199
关键词：
Accident and Emergency department Acute Acute Renal Failure with Renal Papillary Necrosis Algorithms Artificial Intelligence Assessment tool Blood Boston Caring Charge Clinical Clinical Data Clinical Investigator Clinical Trials Design Collection Communities Computers Cost Savings Data Data Set Databases Diagnosis Education Electronic Health Record Erythrocyte Transfusion Feces Gastrointestinal Diseases Gastrointestinal Hemorrhage Goals Health system Healthcare Hematochezia Hemorrhage Hemostatic Agents Hospital Costs Hospitalization Hospitals Intervention Learning Length of Stay Location Lower Gastrointestinal Tract Machine Learning Massachusetts Medical Medical center Melena Mentors Mentorship Modeling Outcome Outpatients Patient Triage Patient risk Patient-Focused Outcomes Patients Performance Pilot Projects Process Provider Recommendation Reporting Research Research Personnel Resources Risk Risk Assessment Sepsis Site Source Standardization Symptoms System Time Training Transfusion United States National Institutes of Health Update Validation Work acute symptom base clinical decision support clinical practice clinical risk computer science data modeling deep learning deep learning algorithm deep learning model design electronic health record system feasibility testing field study gastrointestinal gastrointestinal symptom health assessment implementation science insight longitudinal analysis machine learning algorithm machine learning model machine learning prediction neural network outcome prediction patient health information patient stratification point of care predictive modeling prognostic algorithm progression risk prospective risk stratification time use tool tool development transmission process wasting working group

项目摘要

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.

项目总结