Risk Identification of Bronchopulmonary Dysplasia in Premature Infants Based on Machine Learning

Lei, Jia; Sun, Tiankai; Jiang, Yongjiang; Wu, Ping; Fu, Jinjian; Zhang, Tao; McGrath, Eric

doi:10.3389/fped.2021.719352

Cited by 15 publications

(10 citation statements)

References 17 publications

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“…By duplicating all real features and shuffling them sequentially, the Z-value of each attribute is obtained from a random forest model in each iteration, and the Z-value of shadow is created by random shuffling of the real features. A real feature is regarded as "important" if its Z-value is greater than the maximal Z-value of shadow features in multiple independent trials [26].…”

Section: Discussionmentioning

confidence: 99%

Machine learning for the prediction of acute kidney injury in patients with sepsis

Yue

Huang

et al. 2022

J Transl Med

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Background Acute kidney injury (AKI) is the most common and serious complication of sepsis, accompanied by high mortality and disease burden. The early prediction of AKI is critical for timely intervention and ultimately improves prognosis. This study aims to establish and validate predictive models based on novel machine learning (ML) algorithms for AKI in critically ill patients with sepsis. Methods Data of patients with sepsis were extracted from the Medical Information Mart for Intensive Care III (MIMIC- III) database. Feature selection was performed using a Boruta algorithm. ML algorithms such as logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), decision tree, random forest, Extreme Gradient Boosting (XGBoost), and artificial neural network (ANN) were applied for model construction by utilizing tenfold cross-validation. The performances of these models were assessed in terms of discrimination, calibration, and clinical application. Moreover, the discrimination of ML-based models was compared with those of Sequential Organ Failure Assessment (SOFA) and the customized Simplified Acute Physiology Score (SAPS) II model. Results A total of 3176 critically ill patients with sepsis were included for analysis, of which 2397 cases (75.5%) developed AKI during hospitalization. A total of 36 variables were selected for model construction. The models of LR, KNN, SVM, decision tree, random forest, ANN, XGBoost, SOFA and SAPS II score were established and obtained area under the receiver operating characteristic curves of 0.7365, 0.6637, 0.7353, 0.7492, 0.7787, 0.7547, 0.821, 0.6457 and 0.7015, respectively. The XGBoost model had the best predictive performance in terms of discrimination, calibration, and clinical application among all models. Conclusion The ML models can be reliable tools for predicting AKI in septic patients. The XGBoost model has the best predictive performance, which can be used to assist clinicians in identifying high-risk patients and implementing early interventions to reduce mortality.

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Section: Discussionmentioning

confidence: 99%

Machine learning for the prediction of acute kidney injury in patients with sepsis

Yue

Huang

et al. 2022

J Transl Med

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“…Over the last 3 years, the Boruta algorithm has been used in many fields for feature selection, and it has shown reliability and stability with different evaluation methods ( 29 – 31 ). We also used the Boruta algorithm for the screening of risk genes for ASD in the cuproptosis signaling pathway, and we found that FDX1, DLAT, LIAS, and ATP7B were risk genes.…”

Section: Discussionmentioning

confidence: 99%

Why not try to predict autism spectrum disorder with crucial biomarkers in cuproptosis signaling pathway?

Zhou

Gao²

2022

Front. Psychiatry

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The exact pathogenesis of autism spectrum disorder (ASD) is still unclear, yet some potential mechanisms may not have been evaluated before. Cuproptosis is a novel form of regulated cell death reported this year, and no study has reported the relationship between ASD and cuproptosis. This study aimed to identify ASD in suspected patients early using machine learning models based on biomarkers of the cuproptosis pathway. We collected gene expression profiles from brain samples from ASD model mice and blood samples from humans with ASD, selected crucial genes in the cuproptosis signaling pathway, and then analysed these genes with different machine learning models. The accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curves of the machine learning models were estimated in the training, internal validation, and external validation cohorts. Differences between models were determined with Bonferroni’s test. The results of screening with the Boruta algorithm showed that FDX1, DLAT, LIAS, and ATP7B were crucial genes in the cuproptosis signaling pathway for ASD. All selected genes and corresponding proteins were also expressed in the human brain. The k-nearest neighbor, support vector machine and random forest models could identify approximately 72% of patients with ASD. The artificial neural network (ANN) model was the most suitable for the present data because the accuracy, sensitivity, and specificity were 0.90, 1.00, and 0.80, respectively, in the external validation cohort. Thus, we first report the prediction of ASD in suspected patients with machine learning methods based on crucial biomarkers in the cuproptosis signaling pathway, and these findings may contribute to investigations of the potential pathogenesis and early identification of ASD.

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“…The Z-value of each attribute is obtained from the Random Forest model at each iteration by replicating all the true features and disrupting them in order, and the Z-value of the shadow is created by randomly disrupting the true features. A true feature is considered “significant” if its Z-value is greater than the maximum Z-value of the shaded feature across multiple independent trials (Lei et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Machine learning for the prediction of cognitive impairment in older adults

Zeng

Yuan

et al. 2023

Front. Neurosci.

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ObjectiveThe purpose of this study was to develop and validate a predictive model of cognitive impairment in older adults based on a novel machine learning (ML) algorithm.MethodsThe complete data of 2,226 participants aged 60–80 years were extracted from the 2011–2014 National Health and Nutrition Examination Survey database. Cognitive abilities were assessed using a composite cognitive functioning score (Z-score) calculated using a correlation test among the Consortium to Establish a Registry for Alzheimer's Disease Word Learning and Delayed Recall tests, Animal Fluency Test, and the Digit Symbol Substitution Test. Thirteen demographic characteristics and risk factors associated with cognitive impairment were considered: age, sex, race, body mass index (BMI), drink, smoke, direct HDL-cholesterol level, stroke history, dietary inflammatory index (DII), glycated hemoglobin (HbA1c), Patient Health Questionnaire-9 (PHQ-9) score, sleep duration, and albumin level. Feature selection is performed using the Boruta algorithm. Model building is performed using ten-fold cross-validation, machine learning (ML) algorithms such as generalized linear model (GLM), random forest (RF), support vector machine (SVM), artificial neural network (ANN), and stochastic gradient boosting (SGB). The performance of these models was evaluated in terms of discriminatory power and clinical application.ResultsThe study ultimately included 2,226 older adults for analysis, of whom 384 (17.25%) had cognitive impairment. After random assignment, 1,559 and 667 older adults were included in the training and test sets, respectively. A total of 10 variables such as age, race, BMI, direct HDL-cholesterol level, stroke history, DII, HbA1c, PHQ-9 score, sleep duration, and albumin level were selected to construct the model. GLM, RF, SVM, ANN, and SGB were established to obtain the area under the working characteristic curve of the test set subjects 0.779, 0.754, 0.726, 0.776, and 0.754. Among all models, the GLM model had the best predictive performance in terms of discriminatory power and clinical application.ConclusionsML models can be a reliable tool to predict the occurrence of cognitive impairment in older adults. This study used machine learning methods to develop and validate a well performing risk prediction model for the development of cognitive impairment in the elderly.

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Risk Identification of Bronchopulmonary Dysplasia in Premature Infants Based on Machine Learning

Cited by 15 publications

References 17 publications

Machine learning for the prediction of acute kidney injury in patients with sepsis

Machine learning for the prediction of acute kidney injury in patients with sepsis

Why not try to predict autism spectrum disorder with crucial biomarkers in cuproptosis signaling pathway?

Machine learning for the prediction of cognitive impairment in older adults

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