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Predicting mortality in patients with nonvariceal upper gastrointestinal bleeding using machine-learning
BACKGROUND: Non-endoscopic risk scores, Glasgow Blatchford (GBS) and admission Rockall (Rock), are limited by poor specificity. The aim of this study was to develop an Artificial Neural Network (ANN) for the non-endoscopic triage of nonvariceal upper gastrointestinal bleeding (NVUGIB), with mortalit...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9982090/ https://www.ncbi.nlm.nih.gov/pubmed/36873879 http://dx.doi.org/10.3389/fmed.2023.1134835 |
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author | Ungureanu, Bogdan Silviu Gheonea, Dan Ionut Florescu, Dan Nicolae Iordache, Sevastita Cazacu, Sergiu Marian Iovanescu, Vlad Florin Rogoveanu, Ion Turcu-Stiolica, Adina |
author_facet | Ungureanu, Bogdan Silviu Gheonea, Dan Ionut Florescu, Dan Nicolae Iordache, Sevastita Cazacu, Sergiu Marian Iovanescu, Vlad Florin Rogoveanu, Ion Turcu-Stiolica, Adina |
author_sort | Ungureanu, Bogdan Silviu |
collection | PubMed |
description | BACKGROUND: Non-endoscopic risk scores, Glasgow Blatchford (GBS) and admission Rockall (Rock), are limited by poor specificity. The aim of this study was to develop an Artificial Neural Network (ANN) for the non-endoscopic triage of nonvariceal upper gastrointestinal bleeding (NVUGIB), with mortality as a primary outcome. METHODS: Four machine learning algorithms, namely, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), logistic regression (LR), K-Nearest Neighbor (K-NN), were performed with GBS, Rock, Beylor Bleeding score (BBS), AIM65, and T-score. RESULTS: A total of 1,096 NVUGIB hospitalized in the Gastroenterology Department of the County Clinical Emergency Hospital of Craiova, Romania, randomly divided into training and testing groups, were included retrospectively in our study. The machine learning models were more accurate at identifying patients who met the endpoint of mortality than any of the existing risk scores. AIM65 was the most important score in the detection of whether a NVUGIB would die or not, whereas BBS had no influence on this. Also, the greater AIM65 and GBS, and the lower Rock and T-score, the higher mortality will be. CONCLUSION: The best accuracy was obtained by the hyperparameter-tuned K-NN classifier (98%), giving the highest precision and recall on the training and testing datasets among all developed models, showing that machine learning can accurately predict mortality in patients with NVUGIB. |
format | Online Article Text |
id | pubmed-9982090 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99820902023-03-04 Predicting mortality in patients with nonvariceal upper gastrointestinal bleeding using machine-learning Ungureanu, Bogdan Silviu Gheonea, Dan Ionut Florescu, Dan Nicolae Iordache, Sevastita Cazacu, Sergiu Marian Iovanescu, Vlad Florin Rogoveanu, Ion Turcu-Stiolica, Adina Front Med (Lausanne) Medicine BACKGROUND: Non-endoscopic risk scores, Glasgow Blatchford (GBS) and admission Rockall (Rock), are limited by poor specificity. The aim of this study was to develop an Artificial Neural Network (ANN) for the non-endoscopic triage of nonvariceal upper gastrointestinal bleeding (NVUGIB), with mortality as a primary outcome. METHODS: Four machine learning algorithms, namely, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), logistic regression (LR), K-Nearest Neighbor (K-NN), were performed with GBS, Rock, Beylor Bleeding score (BBS), AIM65, and T-score. RESULTS: A total of 1,096 NVUGIB hospitalized in the Gastroenterology Department of the County Clinical Emergency Hospital of Craiova, Romania, randomly divided into training and testing groups, were included retrospectively in our study. The machine learning models were more accurate at identifying patients who met the endpoint of mortality than any of the existing risk scores. AIM65 was the most important score in the detection of whether a NVUGIB would die or not, whereas BBS had no influence on this. Also, the greater AIM65 and GBS, and the lower Rock and T-score, the higher mortality will be. CONCLUSION: The best accuracy was obtained by the hyperparameter-tuned K-NN classifier (98%), giving the highest precision and recall on the training and testing datasets among all developed models, showing that machine learning can accurately predict mortality in patients with NVUGIB. Frontiers Media S.A. 2023-02-17 /pmc/articles/PMC9982090/ /pubmed/36873879 http://dx.doi.org/10.3389/fmed.2023.1134835 Text en Copyright © 2023 Ungureanu, Gheonea, Florescu, Iordache, Cazacu, Iovanescu, Rogoveanu and Turcu-Stiolica. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Ungureanu, Bogdan Silviu Gheonea, Dan Ionut Florescu, Dan Nicolae Iordache, Sevastita Cazacu, Sergiu Marian Iovanescu, Vlad Florin Rogoveanu, Ion Turcu-Stiolica, Adina Predicting mortality in patients with nonvariceal upper gastrointestinal bleeding using machine-learning |
title | Predicting mortality in patients with nonvariceal upper gastrointestinal bleeding using machine-learning |
title_full | Predicting mortality in patients with nonvariceal upper gastrointestinal bleeding using machine-learning |
title_fullStr | Predicting mortality in patients with nonvariceal upper gastrointestinal bleeding using machine-learning |
title_full_unstemmed | Predicting mortality in patients with nonvariceal upper gastrointestinal bleeding using machine-learning |
title_short | Predicting mortality in patients with nonvariceal upper gastrointestinal bleeding using machine-learning |
title_sort | predicting mortality in patients with nonvariceal upper gastrointestinal bleeding using machine-learning |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9982090/ https://www.ncbi.nlm.nih.gov/pubmed/36873879 http://dx.doi.org/10.3389/fmed.2023.1134835 |
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