Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death

Chung, Chi-Li; Chan, Lung; Bamodu, Oluwaseun Adebayo; Hong, Chien Tai; Chiu, Hung Wen

doi:10.1038/s41598-020-77546-5

Cited by 36 publications

(32 citation statements)

References 37 publications

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“…This study is the first to investigate the plasma EV cytokine profile in patients with PD, thus providing a novel platform to assess inflammation in these patients. Although any single cytokine fails to distinguish patients with PD from controls, evaluating them as a panel and analyzing them by using an artificial intelligence-assisted artificial neural network could effectively predict the neurological outcome after stroke [ 23 , 32 ]. Moreover, the significantly different plasma EV cytokine profiles in patients with PD with and without cognitive deficit suggest the role of inflammation in PD progression.…”

Section: Discussionmentioning

confidence: 99%

Cytokine Profile in Plasma Extracellular Vesicles of Parkinson’s Disease and the Association with Cognitive Function

Chan

Chung

Chen

et al. 2021

Cells

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Plasma extracellular vesicles (EVs) containing various molecules, including cytokines, can reflect the intracellular condition and participate in cell-to-cell signaling, thus emerging as biomarkers for Parkinson’s disease (PD). Inflammation may be a crucial risk factor for PD development and progression. The present study investigated the role of plasma EV cytokines as the biomarkers of PD. This cross-sectional study recruited 113 patients with PD, with mild to moderate stage disease, and 48 controls. Plasma EVs were isolated, and the levels of cytokines, including pro-interleukin (IL)-1β, IL-6, IL-10, tumor necrosis factor (TNF)-α, and transforming growth factor (TGF)-β1, were evaluated. Patients with PD had significantly increased plasma EV pro-IL-1β and TNF-α levels compared with controls after adjustment for age and sex. Despite the lack of a significant association between plasma EV cytokines and motor symptom severity in patients with PD, cognitive dysfunction severity, assessed using the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment, was significantly associated with plasma EV pro-IL-1β, IL-6, IL-10, and TNF-α levels. This association was PD specific and not found in controls. Furthermore, patients with PD cognitive deficit (MMSE < 26) exhibited a distinguished EV cytokine profile compared to those without cognitive deficit. The findings support the concept of inflammatory pathogenesis in the development and progression of PD and indicate that plasma EV cytokines may serve as PD biomarkers in future.

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Section: Discussionmentioning

confidence: 99%

Cytokine Profile in Plasma Extracellular Vesicles of Parkinson’s Disease and the Association with Cognitive Function

Chan

Chung

Chen

et al. 2021

Cells

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“…However, the learning result of additional ANN models performed with the scaling input variable in our study were not better than the crude ANN model. In other DL studies relating to stroke, there was no mention of the effect of neural network scaling on DL performance [11,39]. Ahsan et al reported the effect of scaling on performance in various ML methods [40].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Hemorrhagic Transformation after Ischemic Stroke Using Machine Learning

Choi

Seo

Kim

et al. 2021

JPM

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Hemorrhagic transformation (HT) is one of the leading causes of a poor prognostic marker after acute ischemic stroke (AIS). We compared the performances of the several machine learning (ML) algorithms to predict HT after AIS using only structured data. A total of 2028 patients with AIS, who were admitted within seven days of symptoms onset, were included in this analysis. HT was defined based on the criteria of the European Co-operative Acute Stroke Study-II trial. The whole dataset was randomly divided into a training and a test dataset with a 7:3 ratio. Binary logistic regression, support vector machine, extreme gradient boosting, and artificial neural network (ANN) algorithms were used to assess the performance of predicting the HT occurrence after AIS. Five-fold cross validation and a grid search technique were used to optimize the hyperparameters of each ML model, which had its performance measured by the area under the receiver operating characteristic (AUROC) curve. Among the included AIS patients, the mean age and number of male subjects were 69.6 years and 1183 (58.3%), respectively. HT was observed in 318 subjects (15.7%). There were no significant differences in corresponding variables between the training and test dataset. Among all the ML algorithms, the ANN algorithm showed the best performance in terms of predicting the occurrence of HT in our dataset (0.844). Feature scaling including standardization and normalization, and the resampling strategy showed no additional improvement of the ANN’s performance. The ANN-based prediction of HT after AIS showed better performance than the conventional ML algorithms. Deep learning may be used to predict important outcomes for structured data-based prediction.

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“…The variables were screened based on previously published articles and clinical experience [ 6 , 11 , 20 ]. Accordingly, 25 clinical variables were used in this study, and the data of the 216 patients were randomly divided into training (n = 168, 80%) and test (n = 48, 20%) sets using the Kennard-Stone algorithm.…”

Section: Methodsmentioning

confidence: 99%

Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions

Zhou

Jiao

et al. 2022

PLoS ONE

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Background Generalized regression neural network (GRNN) and logistic regression (LR) are extensively used in the medical field; however, the better model for predicting stroke outcome has not been established. The primary goal of this study was to compare the accuracies of GRNN and LR models to identify the most optimal model for the prediction of acute stroke outcome, as well as explore useful biomarkers for predicting the prognosis of acute stroke patients. Method In a single-center study, 216 (80% for the training set and 20% for the test set) acute stroke patients admitted to the Shenzhen Second People’s Hospital between December 2019 to June 2021 were retrospectively recruited. The functional outcomes of the patients were measured using Barthel Index (BI) on discharge. A training set was used to optimize the GRNN and LR models. The test set was utilized to validate and compare the performances of GRNN and LR in predicting acute stroke outcome based on the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and the Kappa value. Result The LR analysis showed that age, the National Institute Health Stroke Scale score, BI index, hemoglobin, and albumin were independently associated with stroke outcome. After validating in test set using these variables, we found that the GRNN model showed a better performance based on AUROC (0.931 vs 0.702), sensitivity (0.933 vs 0.700), specificity (0.889 vs 0.722), accuracy (0.896 vs 0.729), and the Kappa value (0.775 vs 0.416) than the LR model. Conclusion Overall, the GRNN model demonstrated superior performance to the LR model in predicting the prognosis of acute stroke patients. In addition to its advantage in not affected by implicit interactions and complex relationship in the data. Thus, we suggested that GRNN could be served as the optimal statistical model for acute stroke outcome prediction. Simultaneously, prospective validation based on more variables of the GRNN model for the prediction is required in future studies.

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Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death

Cited by 36 publications

References 37 publications

Cytokine Profile in Plasma Extracellular Vesicles of Parkinson’s Disease and the Association with Cognitive Function

Cytokine Profile in Plasma Extracellular Vesicles of Parkinson’s Disease and the Association with Cognitive Function

Prediction of Hemorrhagic Transformation after Ischemic Stroke Using Machine Learning

Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions

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