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Zhang Y, Zhu H, Cao T, Zhang L, Chang Y, Liang S, Ma C, Liang F, Song Y, Zhang J, Li C, Jiang C. Rupture-Related Features of Cerebral Arteriovenous Malformations and Their Utility in Predicting Hemorrhage. Stroke 2024; 55:1339-1348. [PMID: 38511314 DOI: 10.1161/strokeaha.123.045456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 02/23/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND Evaluating rupture risk in cerebral arteriovenous malformations currently lacks quantitative hemodynamic and angioarchitectural features necessary for predicting subsequent hemorrhage. We aimed to derive rupture-related hemodynamic and angioarchitectural features of arteriovenous malformations and construct an ensemble model for predicting subsequent hemorrhage. METHODS This retrospective study included 3 data sets, as follows: training and test data sets comprising consecutive patients with untreated cerebral arteriovenous malformations who were admitted from January 2015 to June 2022 and a validation data set comprising patients with unruptured arteriovenous malformations who received conservative treatment between January 2009 and December 2014. We extracted rupture-related features and developed logistic regression (clinical features), decision tree (hemodynamic features), and support vector machine (angioarchitectural features) models. These 3 models were combined into an ensemble model using a weighted soft-voting strategy. The performance of the models in discriminating ruptured arteriovenous malformations and predicting subsequent hemorrhage was evaluated with confusion matrix-related metrics in the test and validation data sets. RESULTS A total of 896 patients (mean±SD age, 28±14 years; 404 women) were evaluated, with 632, 158, and 106 patients in the training, test, and validation data sets, respectively. From the training set, 9 clinical, 10 hemodynamic, and 2912 pixel-based angioarchitectural features were extracted. A logistic regression model was built using 4 selected clinical features (age, nidus size, location, and venous aneurysm), whereas a decision-tree model was constructed from 4 hemodynamic features (outflow time, stasis index, cerebral blood flow, and outflow volume ratio). A support vector machine model was designed using 5 pixel-based angioarchitectural features. In the validation data set, the accuracy, sensitivity, specificity, and area under the curve of the ensemble model for predicting subsequent hemorrhages were 0.840, 0.889, 0.823, and 0.911, respectively. CONCLUSIONS The ensemble model incorporating clinical, hemodynamic, and angioarchitectural features showed favorable performance in predicting subsequent hemorrhage of cerebral arteriovenous malformations.
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Affiliation(s)
- Yupeng Zhang
- Department of Neurosurgery, Beijing Neurosurgical Institute (Y.Z., H.Z., Y.C., Y.S., J.Z., C.J.), Capital Medical University, China
- Department of Neurosurgery, Beijing Tiantan Hospital (Y.Z., H.Z., Y.C., Y.S., J.Z., C.J.), Capital Medical University, China
| | - Haoyu Zhu
- Department of Neurosurgery, Beijing Neurosurgical Institute (Y.Z., H.Z., Y.C., Y.S., J.Z., C.J.), Capital Medical University, China
- Department of Neurosurgery, Beijing Tiantan Hospital (Y.Z., H.Z., Y.C., Y.S., J.Z., C.J.), Capital Medical University, China
| | - Tingliang Cao
- Department of Neurosurgery, Kaifeng Central Hospital, Henan, China (T.C.)
| | - Longhui Zhang
- Department of Neurology, Beijing Tiantan Hospital (L.Z.), Capital Medical University, China
| | - Yuzhou Chang
- Department of Neurosurgery, Beijing Neurosurgical Institute (Y.Z., H.Z., Y.C., Y.S., J.Z., C.J.), Capital Medical University, China
- Department of Neurosurgery, Beijing Tiantan Hospital (Y.Z., H.Z., Y.C., Y.S., J.Z., C.J.), Capital Medical University, China
| | - Shikai Liang
- Department of Neurosurgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, China (S.L., C.M.)
| | - Chao Ma
- Department of Neurosurgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, China (S.L., C.M.)
| | - Fei Liang
- Department of Interventional Radiology and Vascular Surgery, Peking University Third Hospital, Beijing, China (F.L.)
| | - Yuqi Song
- Department of Neurosurgery, Beijing Neurosurgical Institute (Y.Z., H.Z., Y.C., Y.S., J.Z., C.J.), Capital Medical University, China
- Department of Neurosurgery, Beijing Tiantan Hospital (Y.Z., H.Z., Y.C., Y.S., J.Z., C.J.), Capital Medical University, China
| | - Jiarui Zhang
- Department of Neurosurgery, Beijing Neurosurgical Institute (Y.Z., H.Z., Y.C., Y.S., J.Z., C.J.), Capital Medical University, China
- Department of Neurosurgery, Beijing Tiantan Hospital (Y.Z., H.Z., Y.C., Y.S., J.Z., C.J.), Capital Medical University, China
| | - Changxuan Li
- Department of Neurosurgery, The First Affiliated Hospital of Hainan Medical University, Hainan, China (C.L.)
| | - Chuhan Jiang
- Department of Neurosurgery, Beijing Neurosurgical Institute (Y.Z., H.Z., Y.C., Y.S., J.Z., C.J.), Capital Medical University, China
- Department of Neurosurgery, Beijing Tiantan Hospital (Y.Z., H.Z., Y.C., Y.S., J.Z., C.J.), Capital Medical University, China
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Shayegan MJ. A brief review and scientometric analysis on ensemble learning methods for handling COVID-19. Heliyon 2024; 10:e26694. [PMID: 38420425 PMCID: PMC10901105 DOI: 10.1016/j.heliyon.2024.e26694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/07/2024] [Accepted: 02/19/2024] [Indexed: 03/02/2024] Open
Abstract
Numerous efforts and research have been conducted worldwide to combat the coronavirus disease 2019 (COVID-19) pandemic. In this regard, some researchers have focused on deep and machine-learning approaches to discover more about this disease. There have been many articles on using ensemble learning methods for COVID-19 detection. Still, there seems to be no scientometric analysis or a brief review of these researches. Hence, a combined method of scientometric analysis and brief review was used to study the published articles that employed an ensemble learning approach to detect COVID-19. This research used both methods to overcome their limitations, leading to enhanced and reliable outcomes. The related articles were retrieved from the Scopus database. Then a two-step procedure was employed. A concise review of the collected articles was conducted. Then they underwent scientometric and bibliometric analyses. The findings revealed that convolutional neural network (CNN) is the mostly employed algorithm, while support vector machine (SVM), random forest, Resnet, DenseNet, and visual geometry group (VGG) were also frequently used. Additionally, China has had a significant presence in the numerous top-ranking categories of this field of research. Both study phases yielded valuable results and rankings.
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Kwon HJ, Park S, Park YH, Baik SM, Park DJ. Development of blood demand prediction model using artificial intelligence based on national public big data. Digit Health 2024; 10:20552076231224245. [PMID: 38250146 PMCID: PMC10798124 DOI: 10.1177/20552076231224245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 12/15/2023] [Indexed: 01/23/2024] Open
Abstract
Objective Modern healthcare systems face challenges related to the stable and sufficient blood supply of blood due to shortages. This study aimed to predict the monthly blood transfusion requirements in medical institutions using an artificial intelligence model based on national open big data related to transfusion. Methods Data regarding blood types and components in Korea from January 2010 to December 2021 were obtained from the Health Insurance Review and Assessment Service and Statistics Korea. The data were collected from a single medical institution. Using the obtained information, predictive models were developed, including eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and category boosting (CatBoost). An ensemble model was created using these three models. Results The prediction performance of XGBoost, LGBM, and CatBoost demonstrated a mean absolute error ranging from 14.6657 for AB+ red blood cells (RBCs) to 84.0433 for A+ platelet concentrate (PC) and a root mean squared error ranging from 18.5374 for AB+ RBCs to 118.6245 for B+ PC. The error range was further improved by creating ensemble models, wherein the department requesting blood was the most influential parameter affecting transfusion prediction performance for different blood products and types. Except for the department, the features that affected the prediction performance varied for each product and blood type, including the number of RBC antibody screens, crossmatch, nationwide blood donations, and surgeries. Conclusion Based on blood-related open big data, the developed blood-demand prediction algorithm can efficiently provide medical facilities with an appropriate volume of blood ahead of time.
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Affiliation(s)
- Hi Jeong Kwon
- Department of Laboratory Medicine, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Sholhui Park
- Department of Laboratory Medicine, Ewha Womans University College of Medicine, Seoul, Korea
| | - Young Hoon Park
- Division of Hematology–Oncology, Department of Internal Medicine, Ewha Womans University Mokdong Hospital, Seoul, Korea
| | - Seung Min Baik
- Division of Critical Care Medicine, Department of Surgery, College of Medicine, Ewha Womans University, Seoul, Korea
| | - Dong Jin Park
- Department of Laboratory Medicine, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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Shakibfar S, Nyberg F, Li H, Zhao J, Nordeng HME, Sandve GKF, Pavlovic M, Hajiebrahimi M, Andersen M, Sessa M. Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review. Front Public Health 2023; 11:1183725. [PMID: 37408750 PMCID: PMC10319067 DOI: 10.3389/fpubh.2023.1183725] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/31/2023] [Indexed: 07/07/2023] Open
Abstract
Aim To perform a systematic review on the use of Artificial Intelligence (AI) techniques for predicting COVID-19 hospitalization and mortality using primary and secondary data sources. Study eligibility criteria Cohort, clinical trials, meta-analyses, and observational studies investigating COVID-19 hospitalization or mortality using artificial intelligence techniques were eligible. Articles without a full text available in the English language were excluded. Data sources Articles recorded in Ovid MEDLINE from 01/01/2019 to 22/08/2022 were screened. Data extraction We extracted information on data sources, AI models, and epidemiological aspects of retrieved studies. Bias assessment A bias assessment of AI models was done using PROBAST. Participants Patients tested positive for COVID-19. Results We included 39 studies related to AI-based prediction of hospitalization and death related to COVID-19. The articles were published in the period 2019-2022, and mostly used Random Forest as the model with the best performance. AI models were trained using cohorts of individuals sampled from populations of European and non-European countries, mostly with cohort sample size <5,000. Data collection generally included information on demographics, clinical records, laboratory results, and pharmacological treatments (i.e., high-dimensional datasets). In most studies, the models were internally validated with cross-validation, but the majority of studies lacked external validation and calibration. Covariates were not prioritized using ensemble approaches in most of the studies, however, models still showed moderately good performances with Area under the Receiver operating characteristic Curve (AUC) values >0.7. According to the assessment with PROBAST, all models had a high risk of bias and/or concern regarding applicability. Conclusions A broad range of AI techniques have been used to predict COVID-19 hospitalization and mortality. The studies reported good prediction performance of AI models, however, high risk of bias and/or concern regarding applicability were detected.
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Affiliation(s)
- Saeed Shakibfar
- Pharmacovigilance Research Center, Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Huiqi Li
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Jing Zhao
- Pharmacoepidemiology and Drug Safety Research Group, Department of Pharmacy, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
- UiORealArt Convergence Environment, University of Oslo, Oslo, Norway
| | - Hedvig Marie Egeland Nordeng
- Pharmacoepidemiology and Drug Safety Research Group, Department of Pharmacy, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
- UiORealArt Convergence Environment, University of Oslo, Oslo, Norway
| | - Geir Kjetil Ferkingstad Sandve
- UiORealArt Convergence Environment, University of Oslo, Oslo, Norway
- Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | - Milena Pavlovic
- UiORealArt Convergence Environment, University of Oslo, Oslo, Norway
- Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | | | - Morten Andersen
- Pharmacovigilance Research Center, Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
| | - Maurizio Sessa
- Pharmacovigilance Research Center, Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
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Mangoni AA, Zinellu A. An Updated Systematic Review and Meta-Analysis of the Association between the De Ritis Ratio and Disease Severity and Mortality in Patients with COVID-19. Life (Basel) 2023; 13:1324. [PMID: 37374107 DOI: 10.3390/life13061324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 06/01/2023] [Indexed: 06/29/2023] Open
Abstract
Patients with Coronavirus disease 2019 (COVID-19) often have elevations in markers of liver injury, particularly serum aspartate transaminase (AST) and alanine transaminase (ALT). Such alterations may affect the AST/ALT ratio (De Ritis ratio) and, potentially, clinical outcomes. We conducted an updated systematic review and meta-analysis of the association between the De Ritis ratio and COVID-19 severity and mortality in hospitalized patients. PubMed, Web of Science, and Scopus were searched between 1 December 2019 and 15 February 2023. The Joanna Briggs Institute Critical Appraisal Checklist and the Grading of Recommendations, Assessment, Development, and Evaluation were used to assess the risk of bias and the certainty of the evidence, respectively. Twenty-four studies were identified. The De Ritis ratio on admission was significantly higher in patients with severe disease and non-survivors vs. patients with non-severe disease and survivors (15 studies, weighted mean difference = 0.36, 95% CI 0.24 to 0.49, p < 0.001). The De Ritis ratio was also associated with severe disease and/or mortality using odds ratios (1.83, 95% CI 1.40 to 2.39, p ˂ 0.001; nine studies). Similar results were observed using hazard ratios (2.36, 95% CI 1.17 to 4.79, p = 0.017; five studies). In six studies, the pooled area under the receiver operating characteristic curve was 0.677 (95% CI 0.612 to 0.743). In our systematic review and meta-analysis, higher De Ritis ratios were significantly associated with severe disease and mortality in COVID-19 patients. Therefore, the De Ritis ratio can be useful for early risk stratification and management in this patient group (PROSPERO registration number: CRD42023406916).
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Affiliation(s)
- Arduino A Mangoni
- Discipline of Clinical Pharmacology, College of Medicine and Public Health, Flinders University, Bedford Park, SA 5042, Australia
- Department of Clinical Pharmacology, Flinders Medical Centre, Southern Adelaide Local Health Network, Bedford Park, SA 5042, Australia
| | - Angelo Zinellu
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy
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Abegaz KH, Etikan İ. Boosting the Performance of Artificial Intelligence-Driven Models in Predicting COVID-19 Mortality in Ethiopia. Diagnostics (Basel) 2023; 13:diagnostics13040658. [PMID: 36832146 PMCID: PMC9955316 DOI: 10.3390/diagnostics13040658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 01/30/2023] [Accepted: 02/06/2023] [Indexed: 02/12/2023] Open
Abstract
Like other nations around the world, Ethiopia has suffered negative effects from COVID-19. The objective of this study was to predict COVID-19 mortality using Artificial Intelligence (AI)-driven models. Two-year daily recorded data related to COVID-19 were trained and tested to predict mortality using machine learning algorithms. Normalization of features, sensitivity analysis for feature selection, modelling of AI-driven models, and comparing the boosting model with single AI-driven models were the main activities performed in this study. Prediction of COVID-19 mortality was conducted using a combination of four dominant feature variables, and hence, the best determination of coefficient (DC) of AdaBoost, KNN, ANN-6, and SVM in the prediction process were 0.9422, 0.8618, 0.8629, and 0.7171, respectively. The Boosting model improved the performance of the individual AI-driven models KNN, SVM, and ANN-6 by 7.94, 22.51, and 8.02 percent, respectively, at the verification stage using the testing dataset. This suggests that the boosting model has the best performance for prediction of COVID-19 mortality in Ethiopia. As a result, it suggests a promising potential performance of boosting ensemble model to be applied in predicting mortality and cases from similarly recorded daily data to predict mortality due to COVID-19 in other parts of the world.
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Affiliation(s)
- Kedir Hussein Abegaz
- Biostatistics and Health Informatics, Public Health Department, Madda Walabu University, Robe 247, Ethiopia
- Department of Biostatistics, Faculty of Medicine, Near East University, Near East Avenue, Nicosia 99138, Turkey
- Correspondence: ; Tel.: +251-913-012630
| | - İlker Etikan
- Department of Biostatistics, Faculty of Medicine, Near East University, Near East Avenue, Nicosia 99138, Turkey
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Artificial Intelligence-Driven Ensemble Model for Predicting Mortality Due to COVID-19 in East Africa. Diagnostics (Basel) 2022; 12:diagnostics12112861. [PMID: 36428921 PMCID: PMC9689547 DOI: 10.3390/diagnostics12112861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 11/11/2022] [Accepted: 11/14/2022] [Indexed: 11/19/2022] Open
Abstract
East Africa was not exempt from the devastating effects of COVID-19, which led to the nearly complete cessation of social and economic activities worldwide. The objective of this study was to predict mortality due to COVID-19 using an artificial intelligence-driven ensemble model in East Africa. The dataset, which spans two years, was divided into training and verification datasets. To predict the mortality, three steps were conducted, which included a sensitivity analysis, the modelling of four single AI-driven models, and development of four ensemble models. Four dominant input variables were selected to conduct the single models. Hence, the coefficients of determination of ANFIS, FFNN, SVM, and MLR were 0.9273, 0.8586, 0.8490, and 0.7956, respectively. The non-linear ensemble approaches performed better than the linear approaches, and the ANFIS ensemble was the best-performing ensemble approach that boosted the predicting performance of the single AI-driven models. This fact revealed the promising capability of ensemble models for predicting the daily mortality due to COVID-19 in other parts of the globe.
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