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Rajwa B, Naved MMA, Adibuzzaman M, Grama AY, Khan BA, Dundar MM, Rochet JC. Identification of predictive patient characteristics for assessing the probability of COVID-19 in-hospital mortality. PLOS DIGITAL HEALTH 2024; 3:e0000327. [PMID: 38652722 PMCID: PMC11037536 DOI: 10.1371/journal.pdig.0000327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 03/06/2024] [Indexed: 04/25/2024]
Abstract
As the world emerges from the COVID-19 pandemic, there is an urgent need to understand patient factors that may be used to predict the occurrence of severe cases and patient mortality. Approximately 20% of SARS-CoV-2 infections lead to acute respiratory distress syndrome caused by the harmful actions of inflammatory mediators. Patients with severe COVID-19 are often afflicted with neurologic symptoms, and individuals with pre-existing neurodegenerative disease have an increased risk of severe COVID-19. Although collectively, these observations point to a bidirectional relationship between severe COVID-19 and neurologic disorders, little is known about the underlying mechanisms. Here, we analyzed the electronic health records of 471 patients with severe COVID-19 to identify clinical characteristics most predictive of mortality. Feature discovery was conducted by training a regularized logistic regression classifier that serves as a machine-learning model with an embedded feature selection capability. SHAP analysis using the trained classifier revealed that a small ensemble of readily observable clinical features, including characteristics associated with cognitive impairment, could predict in-hospital mortality with an accuracy greater than 0.85 (expressed as the area under the ROC curve of the classifier). These findings have important implications for the prioritization of clinical measures used to identify patients with COVID-19 (and, potentially, other forms of acute respiratory distress syndrome) having an elevated risk of death.
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Affiliation(s)
- Bartek Rajwa
- Bindley Bioscience Center, Purdue University, West Lafayette, Indiana, United States of America
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, United States of America
| | | | - Mohammad Adibuzzaman
- Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, Oregon, United States of America
| | - Ananth Y. Grama
- Dept. of Computer Science, Purdue University, West Lafayette, Indiana, United States of America
| | - Babar A. Khan
- Regenstrief Institute, Indianapolis, Indiana, United States of America
| | - M. Murat Dundar
- Dept. of Computer and Information Science, IUPUI, Indianapolis, Indiana, United States of America
| | - Jean-Christophe Rochet
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, United States of America
- Borch Dept. of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana, United States of America
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Anku EK, Duah HO. Predicting and identifying factors associated with undernutrition among children under five years in Ghana using machine learning algorithms. PLoS One 2024; 19:e0296625. [PMID: 38349921 PMCID: PMC10863846 DOI: 10.1371/journal.pone.0296625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 11/13/2023] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Undernutrition among children under the age of five is a major public health concern, especially in developing countries. This study aimed to use machine learning (ML) algorithms to predict undernutrition and identify its associated factors. METHODS Secondary data analysis of the 2017 Multiple Indicator Cluster Survey (MICS) was performed using R and Python. The main outcomes of interest were undernutrition (stunting: height-for-age (HAZ) < -2 SD; wasting: weight-for-height (WHZ) < -2 SD; and underweight: weight-for-age (WAZ) < -2 SD). Seven ML algorithms were trained and tested: linear discriminant analysis (LDA), logistic model, support vector machine (SVM), random forest (RF), least absolute shrinkage and selection operator (LASSO), ridge regression, and extreme gradient boosting (XGBoost). The ML models were evaluated using the accuracy, confusion matrix, and area under the curve (AUC) receiver operating characteristics (ROC). RESULTS In total, 8564 children were included in the final analysis. The average age of the children was 926 days, and the majority were females. The weighted prevalence rates of stunting, wasting, and underweight were 17%, 7%, and 12%, respectively. The accuracies of all the ML models for wasting were (LDA: 84%; Logistic: 95%; SVM: 92%; RF: 94%; LASSO: 96%; Ridge: 84%, XGBoost: 98%), stunting (LDA: 86%; Logistic: 86%; SVM: 98%; RF: 88%; LASSO: 86%; Ridge: 86%, XGBoost: 98%), and for underweight were (LDA: 90%; Logistic: 92%; SVM: 98%; RF: 89%; LASSO: 92%; Ridge: 88%, XGBoost: 98%). The AUC values of the wasting models were (LDA: 99%; Logistic: 100%; SVM: 72%; RF: 94%; LASSO: 99%; Ridge: 59%, XGBoost: 100%), for stunting were (LDA: 89%; Logistic: 90%; SVM: 100%; RF: 92%; LASSO: 90%; Ridge: 89%, XGBoost: 100%), and for underweight were (LDA: 95%; Logistic: 96%; SVM: 100%; RF: 94%; LASSO: 96%; Ridge: 82%, XGBoost: 82%). Age, weight, length/height, sex, region of residence and ethnicity were important predictors of wasting, stunting and underweight. CONCLUSION The XGBoost model was the best model for predicting wasting, stunting, and underweight. The findings showed that different ML algorithms could be useful for predicting undernutrition and identifying important predictors for targeted interventions among children under five years in Ghana.
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Affiliation(s)
- Eric Komla Anku
- Dietherapy and Nutrition, Cape Coast Teaching Hospital, Cape Coast, Ghana
| | - Henry Ofori Duah
- University of Cincinnati College of Nursing, Cincinnati, Ohio, United States of America
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Hussain S, Songhua X, Aslam MU, Hussain F. Clinical predictions of COVID-19 patients using deep stacking neural networks. J Investig Med 2024; 72:112-127. [PMID: 37712431 DOI: 10.1177/10815589231201103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/16/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic, which emerged in late 2019, has caused millions of infections and fatalities globally, disrupting various aspects of human society, including socioeconomic, political, and educational systems. One of the key challenges during the COVID-19 pandemic is accurately predicting the clinical development and outcome of the infected patients. In response, scientists and medical professionals globally have mobilized to develop prognostic strategies such as risk scores, biomarkers, and machine learning models to predict the clinical course and outcomes of COVID-19 patients. In this contribution, we deployed a mathematical approach called matrix factorization feature selection to select the most relevant features from the anonymized laboratory biomarkers and demographic data of COVID-19 patients. Based on these features, developed a model that leverages the deep stacking neural network (DSNN) to aid in clinical care by predicting patients' mortality risk. To gauge the performance of our suggested model, performed a comparative analysis with principal component analysis plus support vector machine, deep learning, and random forest, achieving outstanding performances. The DSNN model outperformed all the other models in terms of area under the curve (96.0%), F1-score (98.1%), recall (98.5%), accuracy (99.0%), precision (97.7%), specificity (97.0%), and maximum probability of correction decision (93.4%). Our model outperforms the clinical predictive models regarding patient mortality risk and classification in the literature. Therefore, we conclude that our robust model can help healthcare professionals to manage COVID-19 patients more effectively. We expect that early prediction of COVID-19 patients and preventive interventions can reduce the mortality risk of patients.
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Affiliation(s)
- Sajid Hussain
- School of Mathematics and Statistics XJTU, Xian, Shaanxi, China
| | - Xu Songhua
- School of Mathematics and Statistics XJTU, Xian, Shaanxi, China
| | | | - Fida Hussain
- School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Nuevo León, Mexico
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Panç K, Hürsoy N, Başaran M, Yazici MM, Kaba E, Nalbant E, Gündoğdu H, Gürün E. Predicting COVID-19 Outcomes: Machine Learning Predictions Across Diverse Datasets. Cureus 2023; 15:e50932. [PMID: 38249212 PMCID: PMC10800012 DOI: 10.7759/cureus.50932] [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] [Accepted: 12/21/2023] [Indexed: 01/23/2024] Open
Abstract
Background The COVID-19 infection has spread rapidly since its emergence and has affected a large part of the global population. With the increasing number of cases, researchers are trying to predict the prognosis of patients by using different data with artificial intelligence methods such as machine learning (ML). In this study, we aimed to predict mortality risk in COVID-19 patients using ML algorithms with different datasets. Methodology In this retrospective study, we evaluated the fever, oxygen saturation, laboratory results, thorax computed tomography (CT) findings, and comorbid diseases at admission to the hospital of 404 patients whose diagnosis was confirmed by the reverse transcription polymerase chain reaction test. Different datasets were created by combining the data. The Synthetic Minority Oversampling Technique was used to reduce the imbalance in the dataset. K-nearest neighbors, support vector machine, stochastic gradient descent, random forest, neural network, naive Bayes, logistic regression, gradient boosting, XGBoost, and AdaBoost models were used to create the ML algorithm, and the accuracy rates of mortality prediction were compared. Results When the dataset was created with CT parenchyma score, pulmonary artery and inferior vena cava diameters, and laboratory results, mortality was predicted with an accuracy of 98.4% with the gradient boosting model. Conclusions The study demonstrates that patient prognosis can be accurately predicted using simple measurements from thorax CT scans and laboratory findings.
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Affiliation(s)
- Kemal Panç
- Radiology, Recep Tayyip Erdoğan Education and Research Hospital, Rize, TUR
| | - Nur Hürsoy
- Radiology, Recep Tayyip Erdoğan Education and Research Hospital, Rize, TUR
| | - Mustafa Başaran
- Radiology, Recep Tayyip Erdoğan Education and Research Hospital, Rize, TUR
| | - Mümin Murat Yazici
- Emergency Medicine, Recep Tayyip Erdoğan Education and Research Hospital, Rize, TUR
| | - Esat Kaba
- Radiology, Recep Tayyip Erdoğan Education and Research Hospital, Rize, TUR
| | | | - Hasan Gündoğdu
- Radiology, Recep Tayyip Erdoğan Education and Research Hospital, Rize, TUR
| | - Enes Gürün
- Radiology, Samsun University, Samsun, TUR
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Usategui I, Barbado J, Torres AM, Cascón J, Mateo J. Machine learning, a new tool for the detection of immunodeficiency patterns in systemic lupus erythematosus. J Investig Med 2023; 71:742-752. [PMID: 37158077 DOI: 10.1177/10815589231171404] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Systemic lupus erythematosus (SLE) is a complex autoimmune disease that affects several organs and causes variable clinical symptoms. Early diagnosis is currently the most effective way to save the lives of patients with SLE. But it is very difficult to detect in the early stages of the disease. Because of this, this study proposes a machine learning system to help diagnose patients with SLE. To carry out the research, the extreme gradient boosting method has been implemented due to its performance characteristics, as it allows high performance, scalability, accuracy, and low computational load. From this method we try to recognize patterns in the data obtained from patients, which allow the classification of SLE patients with high accuracy and differentiate these patients from controls. Several machine learning methods have been analyzed in this study. The proposed method achieves a higher prediction value of patients who may suffer from SLE than the rest of the compared systems. The proposed algorithm achieved an improvement in accuracy of 4.49% over k-Nearest Neighbors. As for the Support Vector Machine and Gaussian Naive Bayes (GNB) methods, they achieved a lower performance than the proposed one, reaching values of 83% and 81%, respectively. It should be noted that the proposed system showed a higher area under the curve (90%) and a balanced accuracy (90%) than the other machine learning methods. This study shows the usefulness of ML techniques for identifying and predicting SLE patients. These results demonstrate the possibility of developing automatic diagnostic support systems for SLE patients based on machine learning techniques.
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Affiliation(s)
- Iciar Usategui
- Internal Medicine Department, Hospital Clínico Universitario de Valladolid, Valladolid, Spain
| | - Julia Barbado
- Autoimmune Diseases Unit, Río Hortega University Hospital, Valladolid, Spain
| | - Ana María Torres
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, Cuenca, Spain
| | - Joaquín Cascón
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, Cuenca, Spain
| | - Jorge Mateo
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, Cuenca, Spain
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Qing X, Jiang J, Yuan C, Xie K, Wang K. Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer. Front Endocrinol (Lausanne) 2023; 14:1222072. [PMID: 37664853 PMCID: PMC10471966 DOI: 10.3389/fendo.2023.1222072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 08/03/2023] [Indexed: 09/05/2023] Open
Abstract
Background Accumulative studies have demonstrated the close relationship between tumor immunity and pyroptosis, apoptosis, and necroptosis. However, the role of PANoptosis in gastric cancer (GC) is yet to be fully understood. Methods This research attempted to identify the expression patterns of PANoptosis regulators and the immune landscape in GC by integrating the GSE54129 and GSE65801 datasets. We analyzed GC specimens and established molecular clusters associated with PANoptosis-related genes (PRGs) and corresponding immune characteristics. The differentially expressed genes were determined with the WGCNA method. Afterward, we employed four machine learning algorithms (Random Forest, Support Vector Machine, Generalized linear Model, and eXtreme Gradient Boosting) to select the optimal model, which was validated using nomogram, calibration curve, decision curve analysis (DCA), and two validation cohorts. Additionally, this study discussed the relationship between infiltrating immune cells and variables in the selected model. Results This study identified dysregulated PRGs and differential immune activities between GC and normal samples, and further identified two PANoptosis-related molecular clusters in GC. These clusters demonstrated remarkable immunological heterogeneity, with Cluster1 exhibiting abundant immune infiltration. The Support Vector Machine signature was found to have the best discriminative ability, and a 5-gene-based SVM signature was established. This model showed excellent performance in the external validation cohorts, and the nomogram, calibration curve, and DCA indicated its reliability in predicting GC patterns. Further analysis confirmed that the 5 selected variables were remarkably related to infiltrating immune cells and immune-related pathways. Conclusion Taken together, this work demonstrates that the PANoptosis pattern has the potential as a stratification tool for patient risk assessment and a reflection of the immune microenvironment in GC.
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Affiliation(s)
- Xin Qing
- Clinical Laboratory, Boai Hospital of Zhongshan Affiliated to Southern Medical University, Zhongshan, China
- West China Hospital, Sichuan University, Chengdu, China
| | - Junyi Jiang
- Clinical Laboratory, Boai Hospital of Zhongshan Affiliated to Southern Medical University, Zhongshan, China
| | - Chunlei Yuan
- Clinical Laboratory, Boai Hospital of Zhongshan Affiliated to Southern Medical University, Zhongshan, China
| | - Kunke Xie
- Clinical Laboratory, Boai Hospital of Zhongshan Affiliated to Southern Medical University, Zhongshan, China
| | - Ke Wang
- Clinical Laboratory, Boai Hospital of Zhongshan Affiliated to Southern Medical University, Zhongshan, China
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Milara J, Martínez-Expósito F, Montero P, Roger I, Bayarri MA, Ribera P, Oishi-Konari MN, Alba-García JR, Zapater E, Cortijo J. N-acetylcysteine Reduces Inflammasome Activation Induced by SARS-CoV-2 Proteins In Vitro. Int J Mol Sci 2022; 23:ijms232314518. [PMID: 36498845 PMCID: PMC9738300 DOI: 10.3390/ijms232314518] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 11/17/2022] [Accepted: 11/19/2022] [Indexed: 11/23/2022] Open
Abstract
Inflammasome activation is one of the first steps in initiating innate immune responses. In this work, we studied the activation of inflammasomes in the airways of critically ill COVID-19 patients and the effects of N-acetylcysteine (NAC) on inflammasomes. Tracheal biopsies were obtained from critically ill patients without COVID-19 and no respiratory disease (control, n = 32), SARS-CoV-2 B.1 variant (n = 31), and B.1.1.7 VOC alpha variant (n = 20) patients. Gene expression and protein expression were measured by RT-qPCR and immunohistochemistry. Macrophages and bronchial epithelial cells were stimulated with different S, E, M, and N SARS-CoV-2 recombinant proteins in the presence or absence of NAC. NLRP3 inflammasome complex was over-expressed and activated in the COVID-19 B.1.1.7 VOC variant and associated with systemic inflammation and 28-day mortality. TLR2/MyD88 and redox NOX4/Nrf2 ratio were also over-expressed in the COVID-19 B.1.1.7 VOC variant. The combination of S-E-M SARS-CoV-2 recombinant proteins increased cytokine release in macrophages and bronchial epithelial cells through the activation of TLR2. NAC inhibited SARS-CoV-2 mosaic (S-E-M)-induced cytokine release and inflammasome activation. In summary, inflammasome is over-activated in severe COVID-19 and increased in B.1.1.7 VOC variant. In addition, NAC can reduce inflammasome activation induced by SARS-CoV-2 in vitro, which may be of potential translational value in COVID-19 patients.
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Affiliation(s)
- Javier Milara
- Department of Pharmacology, Faculty of Medicine, University of Valencia, 46014 Valencia, Spain
- Pharmacy Unit, University General Hospital Consortium, 46014 Valencia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Health Institute Carlos III, 46014 Valencia, Spain
- Correspondence:
| | | | - Paula Montero
- Department of Pharmacology, Faculty of Medicine, University of Valencia, 46014 Valencia, Spain
- Faculty of Health Sciences, Universidad Europea de Valencia, 46010 Valencia, Spain
| | - Inés Roger
- Department of Pharmacology, Faculty of Medicine, University of Valencia, 46014 Valencia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Health Institute Carlos III, 46014 Valencia, Spain
- Faculty of Health Sciences, Universidad Europea de Valencia, 46010 Valencia, Spain
| | - Maria Amparo Bayarri
- Department of Pharmacology, Faculty of Medicine, University of Valencia, 46014 Valencia, Spain
| | - Pilar Ribera
- Department of Pharmacology, Faculty of Medicine, University of Valencia, 46014 Valencia, Spain
| | | | - Jose Ramón Alba-García
- ENT Department, Consorci Hospital General Universitari de Valencia, 46014 Valencia, Spain
| | - Enrique Zapater
- ENT Department, Consorci Hospital General Universitari de Valencia, 46014 Valencia, Spain
| | - Julio Cortijo
- Department of Pharmacology, Faculty of Medicine, University of Valencia, 46014 Valencia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Health Institute Carlos III, 46014 Valencia, Spain
- Research and Teaching Unit, University General Hospital Consortium, 46014 Valencia, Spain
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