Machine learning models predict coagulopathy in spontaneous intracerebral hemorrhage patients in ER

Zhu, Fengping; Pan, Zhiguang; Tang, Ying; Fu, Pengfei; Cheng, Shiduan; Hou, Wenzhong; Zhang, Qi; Huang, Hong; Sun, Yirui

doi:10.1111/cns.13509

Cited by 17 publications

(17 citation statements)

References 49 publications

(59 reference statements)

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“…When integrating those selecting features, the performance of the ML-combined score was superior to the clinical risk metric that is traditionally used to study prognostic outcomes after ICH. Several studies have been published using ML to predict outcomes (primarily survival and function) in patients with ICH (15)(16)(17)(18)(19). The reported performances (AUROC) vary from 0.63 to 0.92, mostly around 0.75-0.85.…”

Section: Discussionmentioning

confidence: 99%

“…ML could incorporate an extensive array of predictors in a non-linear pattern and use multiple interactions to enhance prediction accuracy (10)(11)(12)(13). In recent years, ML has been used for prediction and decision-making in a multitude of ICH (14)(15)(16)(17)(18)(19). Unfortunately, to our knowledge, there has been no effort to use ML to take advantage of blood laboratory data to help physicians predict outcomes at a personalized level in patients with ICH who undergo assessments during routine clinical care.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Robustness of Machine Learning Integration With Routine Laboratory Blood Tests to Predict Inpatient Mortality After Intracerebral Hemorrhage

Chen

et al. 2022

Front. Neurol.

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Objective: The accurate evaluation of outcomes at a personalized level in patients with intracerebral hemorrhage (ICH) is critical clinical implications. This study aims to evaluate how machine learning integrates with routine laboratory tests and electronic health records (EHRs) data to predict inpatient mortality after ICH.Methods: In this machine learning-based prognostic study, we included 1,835 consecutive patients with acute ICH between October 2010 and December 2018. The model building process incorporated five pre-implant ICH score variables (clinical features) and 13 out of 59 available routine laboratory parameters. We assessed model performance according to a range of learning metrics, such as the mean area under the receiver operating characteristic curve [AUROC]. We also used the Shapley additive explanation algorithm to explain the prediction model.Results: Machine learning models using laboratory data achieved AUROCs of 0.71–0.82 in a split-by-year development/testing scheme. The non-linear eXtreme Gradient Boosting model yielded the highest prediction accuracy. In the held-out validation set of development cohort, the predictive model using comprehensive clinical and laboratory parameters outperformed those using clinical alone in predicting in-hospital mortality (AUROC [95% bootstrap confidence interval], 0.899 [0.897–0.901] vs. 0.875 [0.872–0.877]; P <0.001), with over 81% accuracy, sensitivity, and specificity. We observed similar performance in the testing set.Conclusions: Machine learning integrated with routine laboratory tests and EHRs could significantly promote the accuracy of inpatient ICH mortality prediction. This multidimensional composite prediction strategy might become an intelligent assistive prediction for ICH risk reclassification and offer an example for precision medicine.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing Robustness of Machine Learning Integration With Routine Laboratory Blood Tests to Predict Inpatient Mortality After Intracerebral Hemorrhage

Chen

et al. 2022

Front. Neurol.

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“…Previous authors have made great efforts to identify hypokalemia predictors, though the sensitivity and specificity varied between studies. 5 , 7 , 8 , 9 , 10 The difficulty of establishing reliable and efficient prediction models lies in the fact that serum potassium is associated with multiple influences including injury severities, medical histories, and treatment strategies. 11 , 12 Traditional statistical methods are usually retrospective and difficult to analyze a large number of variables.…”

Section: Introductionmentioning

confidence: 99%

Prediction of in‐hospital hypokalemia using machine learning and first hospitalization day records in patients with traumatic brain injury

Zhou

Huang

et al. 2022

CNS Neurosci Ther

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Aims Hypokalemia is a common complication following traumatic brain injury, which may complicate treatment and lead to unfavorable outcomes. Identifying patients at risk of hypokalemia on the first day of admission helps to implement prophylactic treatment, reduce complications, and improve prognosis. Methods This multicenter retrospective study was performed between January 2017 and December 2020 using the electronic medical records of patients admitted due to traumatic brain injury. A propensity score matching approach was adopted with a ratio of 1:1 to overcome overfitting and data imbalance during subgroup analyses. Five machine learning algorithms were applied to generate a best‐performed prediction model for in‐hospital hypokalemia. The internal fivefold cross‐validation and external validation were performed to demonstrate the interpretability and generalizability. Results A total of 4445 TBI patients were recruited for analysis and model generation. Hypokalemia occurred in 46.55% of recruited patients and the incidences of mild, moderate, and severe hypokalemia were 32.06%, 12.69%, and 1.80%, respectively. Hypokalemia was associated with increased mortality, while severe hypokalemia cast greater impacts. The logistic regression algorithm had the best performance in predicting decreased serum potassium and moderate‐to‐severe hypokalemia, with an AUC of 0.73 ± 0.011 and 0.74 ± 0.019, respectively. The prediction model was further verified using two external datasets, including our previous published data and the open‐assessed Medical Information Mart for Intensive Care database. Linearized calibration curves showed no statistical difference (p > 0.05) with perfect predictions. Conclusions The occurrence of hypokalemia following traumatic brain injury can be predicted by first hospitalization day records and machine learning algorithms. The logistic regression algorithm showed an optimal predicting performance verified by both internal and external validation.

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“…Nie et al used nearest neighbors, DT, ANN, AdaBoost, RF to predict inhospital mortality of patients with cerebral hemorrhage in intensive care units, and RF had the highest AUC of 0.819 [17]. The other four studies also achieved good performance (high AUC) using RF [18][19][20][21]. Lim et al used SVM to predict 30-day mortality and 90-day poor functional outcome of ICH patients with good AUC performance of 0.9 and 0.883, respectively [22].…”

Section: Introductionmentioning

confidence: 99%

Joint modeling strategy for using electronic medical records data to build machine learning models: an example of intracerebral hemorrhage

Tang

Wang

Wan

et al. 2022

BMC Med Inform Decis Mak

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Background Outliers and class imbalance in medical data could affect the accuracy of machine learning models. For physicians who want to apply predictive models, how to use the data at hand to build a model and what model to choose are very thorny problems. Therefore, it is necessary to consider outliers, imbalanced data, model selection, and parameter tuning when modeling. Methods This study used a joint modeling strategy consisting of: outlier detection and removal, data balancing, model fitting and prediction, performance evaluation. We collected medical record data for all ICH patients with admissions in 2017–2019 from Sichuan Province. Clinical and radiological variables were used to construct models to predict mortality outcomes 90 days after discharge. We used stacking ensemble learning to combine logistic regression (LR), random forest (RF), artificial neural network (ANN), support vector machine (SVM), and k-nearest neighbors (KNN) models. Accuracy, sensitivity, specificity, AUC, precision, and F1 score were used to evaluate model performance. Finally, we compared all 84 combinations of the joint modeling strategy, including training set with and without cross-validated committees filter (CVCF), five resampling techniques (random under-sampling (RUS), random over-sampling (ROS), adaptive synthetic sampling (ADASYN), Borderline synthetic minority oversampling technique (Borderline SMOTE), synthetic minority oversampling technique and edited nearest neighbor (SMOTEENN)) and no resampling, seven models (LR, RF, ANN, SVM, KNN, Stacking, AdaBoost). Results Among 4207 patients with ICH, 2909 (69.15%) survived 90 days after discharge, and 1298 (30.85%) died within 90 days after discharge. The performance of all models improved with removing outliers by CVCF except sensitivity. For data balancing processing, the performance of training set without resampling was better than that of training set with resampling in terms of accuracy, specificity, and precision. And the AUC of ROS was the best. For seven models, the average accuracy, specificity, AUC, and precision of RF were the highest. Stacking performed best in F1 score. Among all 84 combinations of joint modeling strategy, eight combinations performed best in terms of accuracy (0.816). For sensitivity, the best performance was SMOTEENN + Stacking (0.662). For specificity, the best performance was CVCF + KNN (0.987). Stacking and AdaBoost had the best performances in AUC (0.756) and F1 score (0.602), respectively. For precision, the best performance was CVCF + SVM (0.938). Conclusion This study proposed a joint modeling strategy including outlier detection and removal, data balancing, model fitting and prediction, performance evaluation, in order to provide a reference for physicians and researchers who want to build their own models. This study illustrated the importance of outlier detection and removal for machine learning and showed that ensemble learning might be a good modeling strategy. Due to the low imbalanced ratio (IR, the ratio of majority class and minority class) in this study, we did not find any improvement in models with resampling in terms of accuracy, specificity, and precision, while ROS performed best on AUC.

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Machine learning models predict coagulopathy in spontaneous intracerebral hemorrhage patients in ER

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 17 publications

References 49 publications

Enhancing Robustness of Machine Learning Integration With Routine Laboratory Blood Tests to Predict Inpatient Mortality After Intracerebral Hemorrhage

Enhancing Robustness of Machine Learning Integration With Routine Laboratory Blood Tests to Predict Inpatient Mortality After Intracerebral Hemorrhage

Prediction of in‐hospital hypokalemia using machine learning and first hospitalization day records in patients with traumatic brain injury

Joint modeling strategy for using electronic medical records data to build machine learning models: an example of intracerebral hemorrhage

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