Cited by 228Gastrointestinal Bleeding Diagnosis and TreatmentHelicobacter pylori-related gastroenterology studiesArtificial Intelligence in Healthcare

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Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding

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Shung, Dennis L., Au, Benjamin, Taylor, Richard Andrew, Tay, J. Kenneth, Laursen, Stig B., Stanley, Adrian J., Dalton, Harry R., Ngu, Jeffrey, Schultz, Michael, Laine, Loren·2020·Gastroenterology

DOI 10.1053/j.gastro.2019.09.009PMC PMC7004228

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Abstract

Background & Aims: 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. Methods: 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. Results: 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). Conclusions: 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.

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Additional file 6 of Single-cell RNA sequencing integrated with bulk RNA sequencing analysis identifies a tumor immune microenvironment-related lncRNA signature in lung adenocarcinoma
Ren, Y., Wu, R., Li, C., Liu, L., Li, L., Weng, S., Xu, H., Xing, Z., Zhang, Y., Wang, L., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26696736
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Additional file 12 of Single-cell RNA sequencing integrated with bulk RNA sequencing analysis identifies a tumor immune microenvironment-related lncRNA signature in lung adenocarcinoma
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Additional file 4 of Single-cell RNA sequencing integrated with bulk RNA sequencing analysis identifies a tumor immune microenvironment-related lncRNA signature in lung adenocarcinoma
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Authors: Shung, D. L., Au, B., Taylor, R. A., Tay, J. K., Laursen, S. B., Stanley, A. J., Dalton, H. R., Ngu, J., Schultz, M., & Laine, L.
Affiliations: Yale University (US)
Stanford University (US)
Odense University Hospital (DK)
Glasgow Royal Infirmary (GB)
Royal Cornwall Hospital (GB)
Christchurch Hospital (NZ)
Dunedin Public Hospital (NZ)
Corresponding author: Shung, Dennis L.
Laine, Loren
ORCID: Shung, Dennis L.
Taylor, Richard Andrew
Tay, J. Kenneth
Laursen, Stig B.
Stanley, Adrian J.
Dalton, Harry R.
Schultz, Michael
Laine, Loren
Venue: Gastroenterology
Publisher: Elsevier BV
Published: 2020-01
Volume: 158
Issue: 1
Pages: 160-167
ISSN: 0016-5085 (ISSN-L)
DOI: 10.1053/j.gastro.2019.09.009
PMC: PMC7004228
OpenAlex: W2976369463
Semantic Scholar: 49e263c97948d8177c777a1106e0ab0795effa58
Cited by: 227
References: 54
Citations by year: 2026: 22 · 2025: 30 · 2024: 49 · 2023: 29 · 2022: 31 · 2021: 37 · 2020: 25
FWCI: 21.87
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Gastroenterology (publishedVersion)
PubMed (publishedVersion)
University of Southern Denmark Research Portal (University of Southern Denmark) (publishedVersion, cc-by-nc-nd)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: National Institutes of Health (T32 DK007017)
Topics: Health Sciences › Medicine › Gastroenterology › Gastrointestinal Bleeding Diagnosis and Treatment
Health Sciences › Medicine › Gastroenterology › Helicobacter pylori-related gastroenterology studies
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: upper gastrointestinal bleeding, machine learning prediction models, Glasgow-Blatchford score, Rockall score, AIMS65, gradient boosting, hospital-based intervention, 30-day mortality, external validation, risk stratification, endoscopic hemostasis, blood transfusion, internal validation, sensitivity and specificity
MeSH terms: Machine Learning, Adult, Aged, Aged, 80 and over, Blood Transfusion, Emergency Service, Hospital, Female, Gastrointestinal Hemorrhage, Hemostatic Techniques, Humans, Male, Middle Aged, Models, Biological, Prognosis, ROC Curve, Risk Assessment

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Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding

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