6 results match your criteria: "Yale School of Medicine. Electronic address: dennis.shung@yale.edu.[Affiliation]"
Reply to Li et al, Ren et al, Raghareutai and Kaosombatwattana, and Zhang et al.
Gastroenterology
January 2025
Section of Digestive Diseases, Department of Medicine, Yale School of Medicine; Department of Biomedical Informatics and Data Science, Yale School of Medicine. Electronic address:
Detection of Gastrointestinal Bleeding With Large Language Models to Aid Quality Improvement and Appropriate Reimbursement.
Gastroenterology
January 2025
Section of Digestive Diseases, Department of Medicine, Yale School of Medicine, New Haven, Connecticut; Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut; Clinical and Translational Research Accelerator, Department of Medicine, Yale School of Medicine, New Haven, Connecticut. Electronic address:
Article Synopsis
- The study focuses on using a generative AI pipeline to enhance the identification of overt gastrointestinal bleeding (GIB) in electronic health records, ultimately improving patient management and reimbursement accuracy.
- The pipeline was developed using nursing notes from over 11,000 patients and demonstrated high accuracy in detecting various forms of bleeding, such as melena and hematochezia.
- Results showed that the machine learning model for recurrent bleeding had exceptional diagnostic performance, and the reimbursement algorithm significantly increased average patient reimbursements by up to $3,247, resulting in millions of dollars in total reimbursement.
Validation of an Electronic Health Record-Based Machine Learning Model Compared With Clinical Risk Scores for Gastrointestinal Bleeding.
Gastroenterology
November 2024
Section of Digestive Diseases, Department of Medicine, Yale School of Medicine, New Haven, Connecticut; West Haven Veterans Affairs Medical Center, West Haven, Connecticut.
Article Synopsis
- Guidelines suggest using risk scores to identify very-low-risk patients with gastrointestinal bleeding (GIB) for possible discharge from emergency departments.
- A new machine learning model was developed and tested against existing scores (Glasgow-Blatchford and Oakland) using data from nearly 3,500 patients across different hospitals.
- Results showed the machine learning model performed better in predicting very-low-risk patients, successfully identifying a higher percentage compared to the existing risk scores.
The Reply.
Am J Med
May 2024
Section of Digestive Diseases, Department of Medicine, Yale School of Medicine. Electronic address:
Trends in Upper Gastrointestinal Bleeding in Patients on Primary Prevention Aspirin: A Nationwide Emergency Department Sample Analysis, 2016-2020.
Am J Med
December 2023
Section of Digestive Diseases, Department of Medicine, Yale School of Medicine, New Haven, Conn. Electronic address:
Background: Recent guidelines do not recommend routine use of aspirin for primary cardiovascular prevention (ppASA) and suggest avoidance of ppASA in older individuals due to bleeding risk. However, ppASA is frequently taken without an appropriate indication. Estimates of the incidence of upper gastrointestinal bleeding due to ppASA in the United States are lacking.
View Article and Find Full Text PDFValidation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding.
Gastroenterology
January 2020
Yale School of Medicine, New Haven, Connecticut; Veterans Affairs Connecticut Healthcare System, West Haven, Connecticut. Electronic address:
Background & Aims: Scoring systems are suboptimal for determining risk in patients with upper gastrointestinal bleeding (UGIB); these might be improved by a machine learning model. We used machine learning to develop a model to calculate the risk of hospital-based intervention or death in patients with UGIB and compared its performance with other scoring systems.
Methods: We analyzed data collected from consecutive unselected patients with UGIB from medical centers in 4 countries (the United States, Scotland, England, and Denmark; n = 1958) from March 2014 through March 2015.