Outcome prediction in aneurysmal subarachnoid hemorrhage: a comparison of machine learning methods and established clinico-radiological scores

Dengler, Nora; Madai, Vince I.; Unteroberdörster, Meike; Zihni, Esra; Brune, Sophie Charlotte; Hilbert, Adam; Livne, Michelle; Wolf, Stefan; Vajkoczy, Peter; Frey, Dietmar

doi:10.1007/s10143-020-01453-6

Cited by 31 publications

(23 citation statements)

References 42 publications

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“…A study conducted by Nora Franziska Dengler in 2020 showed that machine learning algorithms including CatBoost tree boosting, support vector machine classifier, Naive Bayes classifier and multilayer perceptron artificial neural net was comparable but not superior to traditional logistic regression in predicting outcome of aSAH patients. 21 And the predictive value of these algorithms was not superior than previously developed clinical-radiographic scores such as Hunt-Huss, WFNS, mFisher and Barrow Neurological Institute (BNI) Score. However, this study only included basic clinical and radiographic features and did not collect intracranial complications and other biochemical parameters.…”

Section: Discussionmentioning

confidence: 83%

XGBoost Machine Learning Algorithm for Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage

Wang¹,

Zhang²,

Shan³

et al. 2022

NDT

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Background Patients suffered aneurysmal subarachnoid hemorrhage (aSAH) usually develop poor survival and functional outcome. Evaluating aSAH patients at high risk of poor outcome is necessary for clinicians to make suitable therapeutical strategy. This study is conducted to develop prognostic model using XGBoost (extreme gradient boosting) algorithm in aSAH. Methods A total of 351 aSAH patients admitted to West China hospital were identified. Patients were divided into training set and test set with ratio of 7:3 to testify the predictive value of XGBoost based prognostic model. Additionally, logistic regression model was also constructed and compared with XGBoost based model. Area under the receiver operating characteristic curve (AUC), sensitivity and specificity were calculated to evaluate the value of XGBoost and logistic regression. Results There were 74 (21.1%) non-survivors and 148 (42.1%) patients with unfavorable functional outcome. Non-survivors had older age (p=0.025), lower Glasgow coma scale (GCS) (p<0.001), higher World Federation of Neurosurgical Societies WFNS score (p<0.001), mFisher score (p<0.001). The incidence of intraventricular hemorrhage (IVH) (p=0.025) and delayed cerebral ischemia (DCI) (p<0.001) was higher in non-survivors than survivors. The AUC of XGBoost model for predicting mortality and unfavorable functional outcome were 0.950 and 0.958, which were higher than 0.767 and 0.829 of logistic regression model. Conclusion XGBoost based model is more precise than logistic regression model in predicting outcome of aSAH patients. Using XGBoost prognostic model is helpful for clinicians to identify high-risk aSAH patients and therefore strengthen medical care.

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Section: Discussionmentioning

confidence: 83%

XGBoost Machine Learning Algorithm for Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage

Wang¹,

Zhang²,

Shan³

et al. 2022

NDT

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“…Different from previous prognostic models for patients after aSAH with small sample sizes, [18][19][20][21] our study used a clinically homogeneous group of patients with a large sample size. For example, Hostettler et al 22 used a decision tree to predict the long-term outcome for the patients after aSAH (n = 329) using both clinical information and laboratory measurements.…”

Section: Discussionmentioning

confidence: 99%

An accurate prognostic prediction for aneurysmal subarachnoid hemorrhage dedicated to patients after endovascular treatment

Han

Xue

et al. 2022

Ther Adv Neurol Disord

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Background: Endovascular treatment for aneurysmal subarachnoid hemorrhage (aSAH) has high fatality and permanent disability rates. It remains unclear how the prognosis is determined by the complex interaction between clinical severity and aneurysm characteristics. Objective: This study aimed to design an accurate prognostic prediction model for aSAH patients after endovascular treatment and elucidate the interaction between clinical severity and aneurysm characteristics. Methods: We used a clinically homogeneous data set with 1029 aSAH patients who received endovascular treatment to develop prognostic models. Aneurysm characteristics were measured by variables, such as aneurysm size, neck size, and dome-to-neck ratio, while clinical severity on admission was measured by both comorbidities and neurological condition. In total, 18 clinical variables were used for prognostic prediction. Considering the imbalance between the favorable and the poor outcomes in this clinical population, both ensemble learning and deep reinforcement learning approaches were used for prediction. Results: The random forest (RF) model was selected as the best approach for the prognostic prediction for all patients and also for patients with good-grade aSAH. Using an independent test data set, the model made accurate predictions (AUC = 0.869 ± 0.036, sensitivity = 0.709 ± 0.087, specificity = 0.805 ± 0.034) with the clinical severity on admission as a leading contributor to the prediction. For patients with good-grade aSAH, the RF model performed the best (AUC = 0.805 ± 0.034, sensitivity = 0.620 ± 0.172, specificity = 0.696 ± 0.043) with aneurysm characteristics as leading contributors. The classic scoring systems failed in this patient group (AUC < 0.600; sensitivity = 0.000, specificity = 1.000). Conclusion: The proposed prognostic prediction model outperformed the classic scoring systems for patients with aSAH after endovascular treatment, especially when the classic scoring systems failed to make any informative prediction for patients with good-grade aSAH, who constitute the majority group (79%) of this clinical population.

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“…In the case of strokes, machine learning algorithms have already been used in clinical applications for automatic diagnosis. ML methods have been applied to the diagnosis of a stroke [ 20 ], prediction of stroke symptom onset [ 21 , 22 ], assessment of stroke severity [ 23 ], analysis of cerebral edema [ 24 ], and outcome prediction [ 25 ]. The data used in these studies were composed of medical records, CT images, or MRI scans.…”

Section: Related Workmentioning

confidence: 99%

Deep-Learning-Based Stroke Screening Using Skeleton Data from Neurological Examination Videos

Lee

Jeon

Jung

et al. 2022

JPM

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According to the Korea Institute for Health and Social Affairs, in 2017, the elderly, aged 65 or older, had an average of 2.7 chronic diseases per person. The concern for the medical welfare of the elderly is increasing due to a low birth rate, an aging population, and the lack of medical personnel. The demand for services that take user age, cognitive capacity, and difficulty into account is rising. As a result, there is an increased demand for smart healthcare systems that can lower hospital admissions and offer patients individualized care. This has motivated us to develop an AI system that can easily screen and manage neurological diseases through videos. As neurological diseases can be diagnosed by visual analysis to some extent, in this study, we set out to estimate the possibility of a person having a neurological disease from videos. Among neurological diseases, we focus on stroke because it is a common condition in the elderly population and results in high mortality and morbidity worldwide. The proposed method consists of three steps: (1) transforming neurological examination videos into landmark data, (2) converting the landmark data into recurrence plots, and (3) estimating the possibility of a stroke using deep neural networks. Major features, such as the hand, face, pupil, and body movements of a person are extracted from test videos taken under several neurological examination protocols using deep-learning-based landmark extractors. Sequences of these landmark data are then converted into recurrence plots, which can be interpreted as images. These images can be fed into convolutional neural networks to classify stroke using feature-fusion techniques. A case study of the application of a disease screening test to assess the capability of the proposed method is presented.

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Outcome prediction in aneurysmal subarachnoid hemorrhage: a comparison of machine learning methods and established clinico-radiological scores

Cited by 31 publications

References 42 publications

XGBoost Machine Learning Algorithm for Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage

XGBoost Machine Learning Algorithm for Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage

An accurate prognostic prediction for aneurysmal subarachnoid hemorrhage dedicated to patients after endovascular treatment

Deep-Learning-Based Stroke Screening Using Skeleton Data from Neurological Examination Videos

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