Nursing Value Analysis and Risk Assessment of Acute Gastrointestinal Bleeding Using Multiagent Reinforcement Learning Algorithm
Gastrointestinal bleeding (GIB) indicates an issue in the digestive system. Blood can be found in feces or vomiting; however, it is not always visible, even if it makes the stool appear darkish or muddy. The bleeding can range in harshness from light to severe and can be dangerous. It is advised tha...
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| Vydané v: | Gastroenterology research and practice Ročník 2022; s. 1 - 12 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Egypt
Hindawi
06.01.2022
John Wiley & Sons, Inc Wiley |
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| ISSN: | 1687-6121, 1687-630X |
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| Abstract | Gastrointestinal bleeding (GIB) indicates an issue in the digestive system. Blood can be found in feces or vomiting; however, it is not always visible, even if it makes the stool appear darkish or muddy. The bleeding can range in harshness from light to severe and can be dangerous. It is advised that nursing value analysis and risk assessment of patients with GIB is essential, but existing risk assessment techniques function inconsistently. Machine learning (ML) has the potential to increase risk evaluation. For evaluating risk in patients with GIB, scoring techniques are ineffective; a machine learning method would help. As a result, we present а unique machine learning-based nursing value analysis and risk assessment framework in this research to construct a model to evaluate the risk of hospital-based interventions or mortality in individuals with GIB and make a comparison to that of other rating systems. Initially, the dataset is collected, and preprocessing is done. Feature extraction is done using local binary patterns (LBP). Classification is performed using a fuzzy support vector machine (FSVM) classifier. For risk assessment and nursing value analysis, machine learning-based prediction using a multiagent reinforcement algorithm is employed. For improving the performance of the proposed system, we use spider monkey optimization (SMO) algorithm. The performance metrics like classification accuracy, area under the receiver-operating characteristic curve (AUROC), area under the curve (AUC), sensitivity, specificity, and precision are analyzed and compared with the traditional approaches. In individuals with GIB, the suggested technique had a good–excellent prognostic efficacy, and it outperformed other traditional models. |
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| AbstractList | Gastrointestinal bleeding (GIB) indicates an issue in the digestive system. Blood can be found in feces or vomiting; however, it is not always visible, even if it makes the stool appear darkish or muddy. The bleeding can range in harshness from light to severe and can be dangerous. It is advised that nursing value analysis and risk assessment of patients with GIB is essential, but existing risk assessment techniques function inconsistently. Machine learning (ML) has the potential to increase risk evaluation. For evaluating risk in patients with GIB, scoring techniques are ineffective; a machine learning method would help. As a result, we present а unique machine learning-based nursing value analysis and risk assessment framework in this research to construct a model to evaluate the risk of hospital-based interventions or mortality in individuals with GIB and make a comparison to that of other rating systems. Initially, the dataset is collected, and preprocessing is done. Feature extraction is done using local binary patterns (LBP). Classification is performed using a fuzzy support vector machine (FSVM) classifier. For risk assessment and nursing value analysis, machine learning-based prediction using a multiagent reinforcement algorithm is employed. For improving the performance of the proposed system, we use spider monkey optimization (SMO) algorithm. The performance metrics like classification accuracy, area under the receiver-operating characteristic curve (AUROC), area under the curve (AUC), sensitivity, specificity, and precision are analyzed and compared with the traditional approaches. In individuals with GIB, the suggested technique had a good–excellent prognostic efficacy, and it outperformed other traditional models. Gastrointestinal bleeding (GIB) indicates an issue in the digestive system. Blood can be found in feces or vomiting; however, it is not always visible, even if it makes the stool appear darkish or muddy. The bleeding can range in harshness from light to severe and can be dangerous. It is advised that nursing value analysis and risk assessment of patients with GIB is essential, but existing risk assessment techniques function inconsistently. Machine learning (ML) has the potential to increase risk evaluation. For evaluating risk in patients with GIB, scoring techniques are ineffective; a machine learning method would help. As a result, we present а unique machine learning-based nursing value analysis and risk assessment framework in this research to construct a model to evaluate the risk of hospital-based interventions or mortality in individuals with GIB and make a comparison to that of other rating systems. Initially, the dataset is collected, and preprocessing is done. Feature extraction is done using local binary patterns (LBP). Classification is performed using a fuzzy support vector machine (FSVM) classifier. For risk assessment and nursing value analysis, machine learning-based prediction using a multiagent reinforcement algorithm is employed. For improving the performance of the proposed system, we use spider monkey optimization (SMO) algorithm. The performance metrics like classification accuracy, area under the receiver-operating characteristic curve (AUROC), area under the curve (AUC), sensitivity, specificity, and precision are analyzed and compared with the traditional approaches. In individuals with GIB, the suggested technique had a good-excellent prognostic efficacy, and it outperformed other traditional models.Gastrointestinal bleeding (GIB) indicates an issue in the digestive system. Blood can be found in feces or vomiting; however, it is not always visible, even if it makes the stool appear darkish or muddy. The bleeding can range in harshness from light to severe and can be dangerous. It is advised that nursing value analysis and risk assessment of patients with GIB is essential, but existing risk assessment techniques function inconsistently. Machine learning (ML) has the potential to increase risk evaluation. For evaluating risk in patients with GIB, scoring techniques are ineffective; a machine learning method would help. As a result, we present а unique machine learning-based nursing value analysis and risk assessment framework in this research to construct a model to evaluate the risk of hospital-based interventions or mortality in individuals with GIB and make a comparison to that of other rating systems. Initially, the dataset is collected, and preprocessing is done. Feature extraction is done using local binary patterns (LBP). Classification is performed using a fuzzy support vector machine (FSVM) classifier. For risk assessment and nursing value analysis, machine learning-based prediction using a multiagent reinforcement algorithm is employed. For improving the performance of the proposed system, we use spider monkey optimization (SMO) algorithm. The performance metrics like classification accuracy, area under the receiver-operating characteristic curve (AUROC), area under the curve (AUC), sensitivity, specificity, and precision are analyzed and compared with the traditional approaches. In individuals with GIB, the suggested technique had a good-excellent prognostic efficacy, and it outperformed other traditional models. |
| Audience | Academic |
| Author | Yin, Changyou Wang, Hongrong Liu, Fang Liu, Xiaoli |
| AuthorAffiliation | 3 Emergency Department, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, China 1 Neurosurgery Department, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, China 2 Department of Infection Management, Dongying People's Hospital, China |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35035476$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1155_2023_9818274 |
| Cites_doi | 10.1016/j.disamonth.2018.02.003 10.1016/j.cgh.2018.09.039 10.1007/s11042-019-07875-9 10.1016/S2468-1253(17)30150-4 10.1111/jgh.15372 10.1016/j.jss.2016.12.032 10.1148/rg.2018170138 10.1016/j.compbiomed.2016.12.009 10.1007/s42452-020-2944-4 10.1136/gutjnl-2019-320002 10.2478/rjim-2019-0016 10.1111/jgh.13830 10.1053/j.gastro.2019.09.009 10.1007/s10620-019-05645-z 10.1080/0952813X.2019.1572657 10.1145/3070861 10.1093/eurheartj/ehz104 10.1016/j.patrec.2019.12.024 10.1016/j.ejim.2018.11.003 10.1136/bmj.i6432 10.1109/ACCESS.2020.2982870 10.1007/s12652-020-02629-0 10.1016/j.cgh.2017.06.029 |
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| Copyright | Copyright © 2022 Fang Liu et al. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Fang Liu et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2022 Fang Liu et al. 2022 |
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| References | 22 12 23 13 24 14 15 16 17 18 19 1 S. Igarashi (11) 2021; 124, article 103950 2 3 4 5 6 7 8 9 20 10 A. Marinescu (21) 2017; 12 38152623 - Gastroenterol Res Pract. 2023 Dec 20;2023:9818274 |
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| Snippet | Gastrointestinal bleeding (GIB) indicates an issue in the digestive system. Blood can be found in feces or vomiting; however, it is not always visible, even if... |
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| SubjectTerms | Algorithms Data mining Endoscopy Fatalities Forecasts and trends Gastrointestinal bleeding Health aspects Illnesses Machine learning Mortality Nursing Optimization techniques Risk assessment Ulcers |
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| Title | Nursing Value Analysis and Risk Assessment of Acute Gastrointestinal Bleeding Using Multiagent Reinforcement Learning Algorithm |
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