Regarding: Shung et al: Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding

Kim, Hyun-seok; Peng, Frederick B.; Cifuentes, Juan D. Gomez

doi:10.1053/j.gastro.2020.01.055

Cited by 6 publications

(1 citation statement)

References 8 publications

(6 reference statements)

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“…When discussing high-risk patients, there is a low probability of discharging and GBS score has shown a specificity of 12% for transfusions, hemostatic interventions as well as death (19). Also, when NVUGIB is associated with liver cirrhosis, mortality might increase due to the underlying disease complications such as hepatic encephalopathy and spontaneous bacterial peritonitis (20).…”

Section: Discussionmentioning

confidence: 99%

Predicting mortality in patients with nonvariceal upper gastrointestinal bleeding using machine-learning

et al. 2023

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BackgroundNon-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.MethodsFour 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.ResultsA 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.ConclusionThe 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.

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Section: Discussionmentioning

confidence: 99%

Predicting mortality in patients with nonvariceal upper gastrointestinal bleeding using machine-learning

et al. 2023

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Analysis of an Economic Coupling Relationship Model of the Coastal Ecological Fragile Zone Based on a Machine Learning Model

Wang

Sun

2022

Wireless Communications and Mobile Computing

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This study is based on a decision tree learning model and integrated system. In order to improve the classification accuracy of the base learner, the diversity of the results is analyzed. Accuracy and diversity are the focus of decision tree classifier research. The balance between decision tree classifiers is a key issue in creating a good ensemble classifier. By reducing the redundant features and training the model with the optimal feature subset, the accuracy of the model can be improved and the running time can be reduced. Normal distribution and generalized error distribution are better. The clustering algorithm based on the K -means partition is a number of compact and independent classification clusters based on the K -means partition algorithm. In order to objectively and accurately treat the machine learning model, an analysis model and economic coupling relationship model were used to evaluate the coastal ecological fragile zone. The error of the result of the classifier after training is smaller.

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Progress in research of risk prediction models for upper gastrointestinal bleeding

Zhang,

Zhu,

Chen

et al. 2023

WCJD

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Regarding: Shung et al: Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding

Cited by 6 publications

References 8 publications

Predicting mortality in patients with nonvariceal upper gastrointestinal bleeding using machine-learning

Predicting mortality in patients with nonvariceal upper gastrointestinal bleeding using machine-learning

Analysis of an Economic Coupling Relationship Model of the Coastal Ecological Fragile Zone Based on a Machine Learning Model

Progress in research of risk prediction models for upper gastrointestinal bleeding

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