Predictive modeling of blood pressure during hemodialysis: a comparison of linear model, random forest, support vector regression, XGBoost, LASSO regression and ensemble method

Huang, Jiun-Chi; Tsai, Yi‐Chun; Wu, Pei‐Yu; Lien, Yu-Hui; Chien, Chih-Yi; Kuo, Chien-Feng; Hung, Jeng-Fung; Chen, Szu-Chia; Kuo, Chao‐Hung

doi:10.1016/j.cmpb.2020.105536

Cited by 103 publications

(66 citation statements)

References 33 publications

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“…ML models include decision tree (DT), RF, extreme gradient boosting (XGBoost), and deep learning. RF and XGBoost are both DT ensemble algorithms [40,41]. However, RF forests rely on bagging, which is a democratic process to "elect" the best decision among the subgroups of trees [40].…”

Section: Model Developmentmentioning

confidence: 99%

Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis

Pattharanitima

Kaewput

Qureshi

et al. 2021

JCM

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Background: Lactic acidosis is the most common cause of anion gap metabolic acidosis in the intensive care unit (ICU), associated with poor outcomes including mortality. We sought to compare machine learning (ML) approaches versus logistic regression analysis for prediction of mortality in lactic acidosis patients admitted to the ICU. Methods: We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify ICU adult patients with lactic acidosis (serum lactate ≥4 mmol/L). The outcome of interest was hospital mortality. We developed prediction models using four ML approaches consisting of random forest (RF), decision tree (DT), extreme gradient boosting (XGBoost), artificial neural network (ANN), and statistical modeling with forward stepwise logistic regression using the testing dataset. We then assessed model performance using area under the receiver operating characteristic curve (AUROC), accuracy, precision, error rate, Matthews correlation coefficient (MCC), F1 score, and assessed model calibration using the Brier score, in the independent testing dataset. Results: Of 1919 lactic acidosis ICU patients, 1535 and 384 were included in the training and testing dataset, respectively. Hospital mortality was 30%. RF had the highest AUROC at 0.83, followed by logistic regression 0.81, XGBoost 0.81, ANN 0.79, and DT 0.71. In addition, RF also had the highest accuracy (0.79), MCC (0.45), F1 score (0.56), and lowest error rate (21.4%). The RF model was the most well-calibrated. The Brier score for RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.15, 0.19, 0.18, 0.19, and 0.16, respectively. The RF model outperformed multivariable logistic regression model, SOFA score (AUROC 0.74), SAP II score (AUROC 0.77), and Charlson score (AUROC 0.69). Conclusion: The ML prediction model using RF algorithm provided the highest predictive performance for hospital mortality among ICU patient with lactic acidosis.

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Section: Model Developmentmentioning

confidence: 99%

Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis

Pattharanitima

Kaewput

Qureshi

et al. 2021

JCM

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“…Vehicle Delay (sec/pc) Queue (avg. number of vehicles) modeling various problems (55,56). The XGBoost method has also revealed its efficient manner in this study and has been able to achieve more accurate estimations under different traffic conditions.…”

Section: Resultsmentioning

confidence: 74%

Vehicle Delay Estimation at Signalized Intersections Using Machine Learning Algorithms

Bağdatlı

Dokuz

2021

Transportation Research Record: Journal of the Transportation R

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Accurate determination of average vehicle delays is significant for effective management of a signalized intersection. The vehicle delays can be determined by field studies, however, this approach is costly and time consuming. Analytical methods which are commonly utilized to estimate delay cannot generate accurate estimates, especially in oversaturated traffic flow conditions. Delay estimation models based on artificial intelligence have been presented in the literature in recent years to estimate the delay more accurately. However, the number of artificial/heuristic intelligence techniques utilized for vehicle delay estimation is limited in the literature. In this study, estimation models are developed using four different machine learning methods—support vector regression (SVR), random forest (RF), k nearest neighbor (kNN), and extreme gradient boosting (XGBoost)—that have not previously been applied in the literature for vehicle delay estimation at signalized intersections. The models were tested with data collected from 12 signalized intersections located in Ankara, the capital of Turkey, and the performance of the models was revealed. The models were furthermore compared with successful delay models from the literature. The developed models, in particular the RF and XGBoost models, showed high performance in estimating the delay at signalized intersections under different traffic conditions. The results indicate that the delay estimation models based on the RF and XGBoost techniques can significantly contribute to both the literature and practice.

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“…LASSO regression is a relatively accurate model variable screening method at present. Compared with other variable screening methods such as ridge regression, LASSO regression can not only screen variables but also adjust complexity, effectively avoiding model over tting [33]. In this study, 17 A total of six variables participating in the model construction were screened by LASSO regression, namely ISH, SBP, DBP, TG, Scr, and CCVD.…”

Section: Discussionmentioning

confidence: 99%

How to Identify White Coat Hypertension at The First Diagnosis: Establishment of A Scoring Model for The Differential Diagnosis of White Coat Hypertension and Sustained Hypertension

Cai

Yan

Chen

et al. 2020

Preprint

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Background: To establish a scoring model for the differential diagnosis of white coat hypertension (WCH) and sustained hypertension (SHT). Methods: 553 adults with elevated office blood pressure, normal renal function and no antihypertensive drugs were included in this study. 17 parameters, such as gender and age, were obtained by questionnaire investigation and biochemical index detection. WCH and SHT were distinguished by 24-hour ambulatory blood pressure monitoring. Participants were randomly divided into a training set (445 cases) and a verification set (108 cases). In the training set, the above parameters were screened by LASSO regression and univariate logistic regression analysis, then, the scoring model was constructed through multivariate logistic regression analysis. ROC curve and calibration curve were used to discuss the discrimination and calibration of this scoring model respectivelyResults: 6 parameters were finally selected, namely isolated systolic hypertension, systolic blood pressure, diastolic blood pressure, triglyceride, serum creatinine, and cardiovascular and cerebrovascular diseases. Multivariate logistic regression was used to establish the scoring model. The R2 and AUC of the scoring model in the training set were 0.163 and 0.705, respectively. In the verification set, the R2 of the scoring model was 0.206, and AUC was 0.718. The calibration test results showed that the scoring model had good stability in both training set and verification set (MSE=0. 001, MAE=0. 014; MSE=0. 001, MAE=0. 025, respectively).Conclusion: A stable scoring model for distinguishing WCH can be established, which can assist clinical medical workers to identify WCH at the first diagnosis.

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Predictive modeling of blood pressure during hemodialysis: a comparison of linear model, random forest, support vector regression, XGBoost, LASSO regression and ensemble method

Cited by 103 publications

References 33 publications

Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis

Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis

Vehicle Delay Estimation at Signalized Intersections Using Machine Learning Algorithms

How to Identify White Coat Hypertension at The First Diagnosis: Establishment of A Scoring Model for The Differential Diagnosis of White Coat Hypertension and Sustained Hypertension

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