scholarly journals Machine Learning Model for Predicting Acute Respiratory Failure in Individuals With Moderate-to-Severe Traumatic Brain Injury

2021 ◽  
Vol 8 ◽  
Author(s):  
Rui Na Ma ◽  
Yi Xuan He ◽  
Fu Ping Bai ◽  
Zhi Peng Song ◽  
Ming Sheng Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Logistic Regression ◽  
Respiratory Failure ◽  
Brain Injury ◽  
Acute Respiratory Failure ◽  
Severe Traumatic Brain Injury ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Out Of Sample

Background: There is a high incidence of acute respiratory failure (ARF) in moderate or severe traumatic brain injury (M-STBI), worsening outcomes. This study aimed to design a predictive model for ARF.Methods: Adult patients with M-STBI [3 ≤ Glasgow Coma Scale (GCS) ≤ 12] with a definite history of brain trauma and abnormal head on CT images, obtained from September 2015 to May 2017, were included. Patients with age >80 years or <18 years, multiple injuries with TBI upon admission, or pregnancy (in women) were excluded. Two models based on machine learning extreme gradient boosting (XGBoost) or logistic regression, respectively, were developed for predicting ARF within 48 h upon admission. These models were evaluated by out-of-sample validation. The samples were assigned to the training and test sets at a ratio of 3:1.Results: In total, 312 patients were analyzed including 132 (42.3%) patients who had ARF. The GCS and the Marshall CT score, procalcitonin (PCT), and C-reactive protein (CRP) on admission significantly predicted ARF. The novel machine learning XGBoost model was superior to logistic regression model in predicting ARF [area under the receiver operating characteristic (AUROC) = 0.903, 95% CI, 0.834–0.966 vs. AUROC = 0.798, 95% CI, 0.697–0.899; p < 0.05].Conclusion: The XGBoost model could better predict ARF in comparison with logistic regression-based model. Therefore, machine learning methods could help to develop and validate novel predictive models.

scholarly journals Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models

PLoS ONE ◽  
2018 ◽  
Vol 13 (11) ◽  
pp. e0207192 ◽  
Author(s):  
Cheng-Shyuan Rau ◽  
Pao-Jen Kuo ◽  
Peng-Chen Chien ◽  
Chun-Ying Huang ◽  
Hsiao-Yun Hsieh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Severe Traumatic Brain Injury ◽  
Mortality Prediction ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Prognostic Value of Admission Parameters in a Machine Learning Predictive Model in Patients With Severe Traumatic Brain Injury and Acute Subdural Hematomas

Neurosurgery ◽  
2019 ◽  
Vol 66 (Supplement_1) ◽  
Author(s):  
Kevin John ◽  
Aaron McPheters ◽  
Andrew Donovan ◽  
Nicolas K Khattar ◽  
Jacob R Shpilberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Predictive Model ◽  
Severe Traumatic Brain Injury ◽  
Acute Subdural Hematoma ◽  
High Morbidity ◽  
Radiographic Findings ◽  
Severe Tbi

Abstract INTRODUCTION Acute subdural hematoma (aSDH) in the context of severe traumatic brain injury (TBI) is a neurosurgical emergency. Predictive models have been used in an attempt to modulate the morbidity and mortality of patient outcomes. We used machine learning (ML) to identify admission risk factors predictive of long-term morbidity in the severe TBI patient population with aSDH. METHODS Between 2013 and 2016, 85 patients with severe TBI and aSDH were included in the analysis. Random forest, ML architecture, was used to create a predictive model of long-term morbidity stratification. About 46 patients were included in the high morbidity group [Glasgow Outcome Scale (GOS) 1-2] and 39 patients were in the low morbidity group (GOS 3-5). We included 30 admission input variables including medical and surgical co-morbidities, neurological examination, laboratory values, and radiographic findings. RESULTS The predictive model showed a 78% precision. The highest scoring input variable was the pupillary examination in predicting high vs low morbidity (bilaterally unreactive vs symmetrically reactive; P < .0001). GCS on admission was higher in the low morbidity group (4 [3-7] vs 7 [3-7]; P < .0101). Rotterdam scores were higher in the high-morbidity group (3 [3-5] vs 4 [4-5]; P < .0032). GCS motor examination on admission was higher in the low-morbidity group (5 [1-5] vs. 2 [1-5]; P < .0106). The basal cisterns were found to be more patent in patients with the low-morbidity group (P = .0012). CONCLUSION ML is an efficient tool that can provide a reasonable level of accuracy in predicting long-term morbidity in patients with severe TBI and aSDH. Monitoring these admission criteria can help with risk-stratification of patients into higher and low risk tracks. Integration of ML into the treatment algorithm may allow the development of more refined guidelines to guide goal-directed therapy.

Using Machine Learning to Predict National Hockey League Average Home Game Attendance

2018 ◽  
Vol 12 (2) ◽  
pp. 85-98 ◽  
Author(s):  
Barry E King ◽  
Jennifer L Rice ◽  
Julie Vaughan
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
National Hockey League ◽  
Gradient Boosting ◽  
Mean Square ◽  
Sample Average ◽  
Extreme Gradient Boosting ◽  
Out Of Sample ◽  
Game Attendance ◽  
Home Game

Research predicting National Hockey League average attendance is presented. The seasons examined are the 2013 hockey season through the beginning of the 2017 hockey season. Multiple linear regression and three machine learning algorithms – random forest, M5 prime, and extreme gradient boosting – are employed to predict out-of-sample average home game attendance. Extreme gradient boosting generated the lowest out-of-sample root mean square error.  The team identifier (team name), the number of Twitter followers (a surrogate for team popularity), median ticket price, and arena capacity have appeared as the top four predictor variables. 

scholarly journals Machine Learning Applications for the Prediction of Bone Cement Leakage in Percutaneous Vertebroplasty

2021 ◽  
Vol 9 ◽  
Author(s):  
Wenle Li ◽  
Jiaming Wang ◽  
Wencai Liu ◽  
Chan Xu ◽  
Wanying Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Bone Cement ◽  
Percutaneous Vertebroplasty ◽  
Cement Leakage ◽  
Gradient Boosting ◽  
Vertebral Compression Fractures ◽  
Good Prediction ◽  
Extreme Gradient Boosting ◽  
The Web

Background: Bone cement leakage is a common complication of percutaneous vertebroplasty and it could be life-threatening to some extent. The aim of this study was to develop a machine learning model for predicting the risk of cement leakage in patients with osteoporotic vertebral compression fractures undergoing percutaneous vertebroplasty. Furthermore, we developed an online calculator for clinical application.Methods: This was a retrospective study including 385 patients, who had osteoporotic vertebral compression fracture disease and underwent surgery at the Department of Spine Surgery, Liuzhou People's Hospital from June 2016 to June 2018. Combing the patient's clinical characteristics variables, we applied six machine learning (ML) algorithms to develop the predictive models, including logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision Tree (DT) and Multilayer perceptron (MLP), which could predict the risk of bone cement leakage. We tested the results with ten-fold cross-validation, which calculated the Area Under Curve (AUC) of the six models and selected the model with the highest AUC as the excellent performing model to build the web calculator.Results: The results showed that Injection volume of bone cement, Surgery time and Multiple vertebral fracture were all independent predictors of bone cement leakage by using multivariate logistic regression analysis in the 385 observation subjects. Furthermore, Heatmap revealed the relative proportions of the 15 clinical variables. In bone cement leakage prediction, the AUC of the six ML algorithms ranged from 0.633 to 0.898, while the RF model had an AUC of 0.898 and was used as the best performing ML Web calculator (https://share.streamlit.io/liuwencai0/pvp_leakage/main/pvp_leakage) was developed to estimate the risk of bone cement leakage that each patient undergoing vertebroplasty.Conclusion: It achieved a good prediction for the occurrence of bone cement leakage with our ML model. The Web calculator concluded based on RF model can help orthopedist to make more individual and rational clinical strategies.

scholarly journals Using machine learning to improve risk prediction in durable left ventricular assist devices

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247866
Author(s):  
Arman Kilic ◽  
Daniel Dochtermann ◽  
Rema Padman ◽  
James K. Miller ◽  
Artur Dubrawski
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Left Ventricular ◽  
Ventricular Assist Devices ◽  
Gradient Boosting ◽  
Left Ventricular Assist Devices ◽  
Ventricular Assist ◽  
Assist Devices ◽  
Extreme Gradient Boosting ◽  
Left Ventricular Assist

Risk models have historically displayed only moderate predictive performance in estimating mortality risk in left ventricular assist device therapy. This study evaluated whether machine learning can improve risk prediction for left ventricular assist devices. Primary durable left ventricular assist devices reported in the Interagency Registry for Mechanically Assisted Circulatory Support between March 1, 2006 and December 31, 2016 were included. The study cohort was randomly divided 3:1 into training and testing sets. Logistic regression and machine learning models (extreme gradient boosting) were created in the training set for 90-day and 1-year mortality and their performance was evaluated after bootstrapping with 1000 replications in the testing set. Differences in model performance were also evaluated in cases of concordance versus discordance in predicted risk between logistic regression and extreme gradient boosting as defined by equal size patient tertiles. A total of 16,120 patients were included. Calibration metrics were comparable between logistic regression and extreme gradient boosting. C-index was improved with extreme gradient boosting (90-day: 0.707 [0.683–0.730] versus 0.740 [0.717–0.762] and 1-year: 0.691 [0.673–0.710] versus 0.714 [0.695–0.734]; each p<0.001). Net reclassification index analysis similarly demonstrated an improvement of 48.8% and 36.9% for 90-day and 1-year mortality, respectively, with extreme gradient boosting (each p<0.001). Concordance in predicted risk between logistic regression and extreme gradient boosting resulted in substantially improved c-index for both logistic regression and extreme gradient boosting (90-day logistic regression 0.536 versus 0.752, 1-year logistic regression 0.555 versus 0.726, 90-day extreme gradient boosting 0.623 versus 0.772, 1-year extreme gradient boosting 0.613 versus 0.742, each p<0.001). These results demonstrate that machine learning can improve risk model performance for durable left ventricular assist devices, both independently and as an adjunct to logistic regression.

scholarly journals Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1909
Author(s):  
Dougho Park ◽  
Eunhwan Jeong ◽  
Haejong Kim ◽  
Hae Wook Pyun ◽  
Haemin Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Functional Outcome ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

Background: Functional outcomes after acute ischemic stroke are of great concern to patients and their families, as well as physicians and surgeons who make the clinical decisions. We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. Methods: This retrospective study used a prospective cohort database. A total of 1066 patients with acute ischemic stroke between January 2019 and March 2021 were included. Variables such as demographic factors, stroke-related factors, laboratory findings, and comorbidities were utilized at the time of admission. Five ML algorithms were applied to predict a favorable functional outcome (modified Rankin Scale 0 or 1) at 3 months after stroke onset. Results: Regularized logistic regression showed the best performance with an area under the receiver operating characteristic curve (AUC) of 0.86. Support vector machines represented the second-highest AUC of 0.85 with the highest F1-score of 0.86, and finally, all ML models applied achieved an AUC > 0.8. The National Institute of Health Stroke Scale at admission and age were consistently the top two important variables for generalized logistic regression, random forest, and extreme gradient boosting models. Conclusions: ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful.

scholarly journals Machine Learning for the Prediction of Progression in Patients with Acute Kidney Injury in Critical Care

2021 ◽  
Author(s):  
Lifan Zhang ◽  
Canzheng Wei ◽  
Yunxia Feng ◽  
Aijia Ma ◽  
Yan Kang
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Critical Care ◽  
Intensive Care ◽  
Logistic Regression Model ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Stage 1

Abstract Background: Acute kidney injury (AKI) is a serve and harmful syndrome in the intensive care unit. The purpose of this study is to develop a prediction model that predict whether patients with AKI stage 1/2 will progress to AKI stage 3. Methods: Patients with AKI stage 1/2, when they were first diagnosed with AKI in the Medical Information Mart for Intensive Care (MIMIC-III), were included. We excluded patients who had underwent RRT or progressed to AKI stage 3 within 72 hours of the first AKI diagnosis. We also excluded patients with chronic kidney disease (CKD). We used the Logistic regression and machine learning extreme gradient boosting (XGBoost) to build two models which can predict patients who will progress to AKI stage 3. Established models were evaluated by cross-validation, receiver operating characteristic curve (ROC), and precision-recall curves (PRC). Results: We included 25711 patients, of whom 2130 (8.3%) progressed to AKI stage 3. Creatinine, multiple organ failure syndromes (MODS), blood urea nitrogen (BUN), sepsis, and respiratory failure were the most important in AKI progression prediction. The XGBoost model has a better performance than the Logistic regression model on predicting AKI stage 3 progression (AU-ROC, 0.926; 95%CI, 0.917 to 0.931 vs. 0.784; 95%CI, 0.771 to 0.796, respectively). Conclusions: The XGboost model can better identify patients with AKI progression than Logistic regression model. Machine learning techniques may improve predictive modeling in medical research. Keywords: Acute kidney injury; Critical care; Logistic Models; Extreme gradient boosting

scholarly journals Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Cheng Qu ◽  
Lin Gao ◽  
Xian-qiang Yu ◽  
Mei Wei ◽  
Guo-quan Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Acute Pancreatitis ◽  
Logistic Regression ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Models ◽  
Extreme Gradient Boosting ◽  
Machine Learning Models

Background. Acute kidney injury (AKI) has long been recognized as a common and important complication of acute pancreatitis (AP). In the study, machine learning (ML) techniques were used to establish predictive models for AKI in AP patients during hospitalization. This is a retrospective review of prospectively collected data of AP patients admitted within one week after the onset of abdominal pain to our department from January 2014 to January 2019. Eighty patients developed AKI after admission (AKI group) and 254 patients did not (non-AKI group) in the hospital. With the provision of additional information such as demographic characteristics or laboratory data, support vector machine (SVM), random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost) were used to build models of AKI prediction and compared to the predictive performance of the classic model using logistic regression (LR). XGBoost performed best in predicting AKI with an AUC of 91.93% among the machine learning models. The AUC of logistic regression analysis was 87.28%. Present findings suggest that compared to the classical logistic regression model, machine learning models using features that can be easily obtained at admission had a better performance in predicting AKI in the AP patients.

scholarly journals A machine learning-based prediction of hospital mortality in patients with postoperative sepsis

2020 ◽  
Author(s):  
Ren-qi Yao ◽  
Xin Jin ◽  
Guo-wei Wang ◽  
Yue Yu ◽  
Guo-sheng Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Hospital Mortality ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Gradient Boosting ◽  
Stepwise Logistic Regression ◽  
Postoperative Sepsis ◽  
Extreme Gradient Boosting ◽  
Stepwise Logistic Regression Model

Abstract Background: The incidence of postoperative sepsis is continually increased, while few studies have specifically focused on the risk factors and clinical outcomes associated with the development of sepsis after surgical procedures. The present study aimed to develop a mathematical model for predicting the in-hospital mortality among patients with postoperative sepsis.Methods: Surgical patients in Medical Information Mart for Intensive Care (MIMIC-III) database who simultaneously fulfilled Sepsis 3.0 as well as Agency for Healthcare Research and Quality (AHRQ) criteria during ICU admission were incorporated. We employed both extreme gradient boosting (XGBoost) and stepwise logistic regression model to predict in-hospital mortality among included patients with postoperative sepsis. Consequently, model performance was assessed from the angles of discrimination and calibration.Results: We included 3713 patients who fulfilled our inclusion criteria, in which 397 (10.7%) patients died during hospitalization, while 3316 (89.3%) of them survived through discharge. Fluid-electrolyte disturbance, coagulopathy, renal replacement therapy (RRT), urine output, and cardiovascular surgery were important features related to the in-hospital mortality. The XGBoost model had a better performance in both discriminatory ability (c-statistics, 0.835 [95% CI, 0.786 to 0.877] vs. c-statistics, 0.737 [95% CI, 0.688 to 0.786]) and goodness of fit (visualized by calibration curve) compared to the stepwise logistic regression model. Conclusion: XGBoost model appears to be a better performance in predicting hospital mortality among postoperative septic patients compared to the conventional stepwise logistic regression model. Machine learning-based algorithm might have significant application in the development of early warning system for septic patients following major operations.

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