Machine-Learning-Based Rehabilitation Prognosis Prediction in Patients with Ischemic Stroke Using Brainstem Auditory Evoked Potential

Sohn, Jangjay; Jung, Il-Young; Ku, Yun Hyi; Kim, Yeong-Wook

doi:10.3390/diagnostics11040673

Cited by 14 publications

(13 citation statements)

References 38 publications

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“…Machine learning models have shown the potential to predict the occurrence or the prognosis of clinical diseases, such as influenza-like illnesses or stroke [ 3 , 4 ]. For the occurrence of respiratory diseases, there exist a few previous studies that applied machine learning to produce forecasting models using air-pollution factors.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models for Predicting the Occurrence of Respiratory Diseases Using Climatic and Air-Pollution Factors

Kwon

Yoon

et al. 2022

Clin Exp Otorhinolaryngol

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Objectives. Because climatic and air-pollution factors are known to influence the occurrence of respiratory diseases, we used these factors to develop machine learning models for predicting the occurrence of respiratory diseases.Methods. We obtained the daily number of respiratory disease patients in Seoul. We used climatic and air-pollution factors to predict the daily number of patients treated for respiratory diseases per 10,000 inhabitants. We applied the reliefbased feature selection algorithm to evaluate the importance of feature selection. We used the gradient boosting and Gaussian process regression (GPR) methods, respectively, to develop two different prediction models. We also employed the holdout cross-validation method, in which 75% of the data was used to train the model, and the remaining 25% was used to test the trained model. We determined the estimated number of respiratory disease patients by applying the developed prediction models to the test set. To evaluate the performance of each model, we calculated the coefficient of determination (R 2 ) and the root mean square error (RMSE) between the original and estimated numbers of respiratory disease patients. We used the Shapley Additive exPlanations (SHAP) approach to interpret the estimated output of each machine learning model. Results.Features with negative weights in the relief-based algorithm were excluded. When applying gradient boosting to unseen test data, R 2 and RMSE were 0.68 and 13.8, respectively. For GPR, the R 2 and RMSE were 0.67 and 13.9, respectively. SHAP analysis showed that reductions in average temperature, daylight duration, average humidity, sulfur dioxide (SO2), total solar insolation amount, and temperature difference increased the number of respiratory disease patients, whereas increases in atmospheric pressure, carbon monoxide (CO), and particulate matter ≤2.5 µm in aerodynamic diameter (PM2.5) increased the number of respiratory disease patients.Conclusions. We successfully developed models for predicting the occurrence of respiratory diseases using climatic and airpollution factors. These models could evolve into public warning systems.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Models for Predicting the Occurrence of Respiratory Diseases Using Climatic and Air-Pollution Factors

Kwon

Yoon

et al. 2022

Clin Exp Otorhinolaryngol

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“…At the end of the 1990s, two pioneering studies had already suggested the use of machine learning algorithms to identify the prognostic factors of neurorehabilitation outcomes [ 7 ] and to predict the following changes in the subacute phase [ 8 ]. The recent development of artificial intelligence (AI) is facilitating the diffusion of machine learning in further studies [ 9 , 10 , 11 , 12 ]. The prognostic factors identified by AI, usually with an accuracy ≥ 70%, were similar to those classically accounted for: clinical test scores at admission, time from stroke onset to rehabilitation admission, age, sex, body mass index, and dysphasia [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Prognostic Factors in Neurorehabilitation of Stroke: A Comparison among Regression, Neural Network, and Cluster Analyses

et al. 2021

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There is a large body of literature reporting the prognostic factors for a positive outcome of neurorehabilitation performed in the subacute phase of stroke. Despite the recent development of algorithms based on neural networks or cluster analysis for the identification of these prognostic factors, the literature lacks a rigorous comparison among classical regression, neural network, and cluster analysis. Moreover, the three methods have rarely been tested on a sample independent from that in which prognostic factors have been identified. This study aims at providing this comparison on a wide sample of data (1522 patients) and testing the results on an independent sample (1000 patients) using 30 variables. The accuracy was similar among regression, neural network, and cluster analyses on the analyzed sample (76.6%, 74%, and 76.1%, respectively), but on the test sample, the accuracy of neural network decreased (70.1%). The three models agreed in identifying older age, severe impairment, unilateral spatial neglect, and total anterior circulation infarcts as important prognostic factors. The binary regression analysis also provided solid results in the test sample, especially in terms of specificity (81.8%). Cluster analysis also showed a high sensitivity in the test sample (82.6%) and allowed a meaningful easy-to-use classification tree to be obtained.

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“…At the same time, we believed that ANE-SS, 10.3389/fped.2022.947693 as a comprehensive predictive indicator, had its advantages in judging the severity of the ANE disease. BAEP might play an auxiliary role in predicting the rehabilitation prognosis of brain injury (28), especially for children with brainstem symptoms who have not been given early diagnosis of ANE.…”

Section: Discussionmentioning

confidence: 99%

Clinical evaluation of acute necrotizing encephalopathy in children

Fan

Huang

et al. 2022

Front. Pediatr.

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ObjectiveAcute necrotizing encephalopathy (ANE) is a rare but severe encephalopathy and is associated with a high morbidity and mortality. We aimed to analyze and compare the clinical features and predictive indicators of pediatric ANE.Materials and methodsThis retrospective study included children with ANE diagnosed at Guangzhou Women and Children’s Medical Center between November 2018 and January 2020. Pediatric patients’ information, including clinical characteristics, laboratory tests, neuroelectrophysiology and brain magnetic resonance imaging (MRI) findings, MRI score, brainstem auditory evoked potential (BAEP) grades, ANE severity scores (ANE-SS), and modified Rankin scale (mRS), were collected.ResultsTwelve ANE patients were included. Among them, one patient (8.3%) died from brainstem dysfunction, one (8.3%) recovered and 10 (83.3%) experienced neurological sequelae. All patients had an initial viral infection and neurological symptoms such as acute disturbance of consciousness (ADOC) or seizure, and the interval from onset of the disease to neurological manifestations was 3 (1.25–3) days. MRI score-I ranged from 1 to 3 (1.8 ± 0.7), MRI score-II ranged from 1 to 4 (2.5 ± 1.1). ANE-SS varied from 1 to 6 (3.9 ± 1.3). The scores of mRS were from 0 to 6 (2.9 ± 1.7). Higher MRI score were associated with worse outcomes, while the BAEP grade and ANE-SS score were not significantly associated with mRS.ConclusionANE is a severe encephalopathy syndrome with rapid progression, resulting in serious neurological sequelae. Compared with BAEP grade and ANE-SS, brain MRI shows more comprehensive advantages in predicting the prognosis of ANE patients. More in-depth research and better indicators are still needed to support the evaluation and treatment of ANE.

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Machine-Learning-Based Rehabilitation Prognosis Prediction in Patients with Ischemic Stroke Using Brainstem Auditory Evoked Potential

Cited by 14 publications

References 38 publications

Machine Learning Models for Predicting the Occurrence of Respiratory Diseases Using Climatic and Air-Pollution Factors

Machine Learning Models for Predicting the Occurrence of Respiratory Diseases Using Climatic and Air-Pollution Factors

Prognostic Factors in Neurorehabilitation of Stroke: A Comparison among Regression, Neural Network, and Cluster Analyses

Clinical evaluation of acute necrotizing encephalopathy in children

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