Ensemble learning accurately predicts the potential benefits of thrombolytic therapy in acute ischemic stroke

Chen, Zhihong; Li, Qingqing; Li, Renyuan; Zhao, Han; Li, Zhaoqing; Zhou, Ying; Bian, Ren-Xiu; Jin, Xinyu; Lou, Min; Bai, Ruiliang

doi:10.21037/qims-21-33

Cited by 6 publications

(11 citation statements)

References 27 publications

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“…The algorithms used by previous research, from the most restrictive to the most flexible, were logistic regression, 8 , 19 – 21 , 24 , 25 , 33 , 36 naïve Bayes classifier, 33 risk score, 9 , 10 , 12 – 16 , 35 nomogram, 22 , 23 , 28 , 30 , 31 , 34 tree-based machine learning models, 29 , 33 , 36 support vector machine, 11 , 17 , 29 and deep learning neural network. 11 , 26 , 27 , 29 , 32 , 33 …”

Section: Related Workmentioning

confidence: 99%

“…Previous studies leveraged machine learning models to assist neurologists in deciding the safety and efficiency for each patient more accurately 8 – 36 : they all simply reused the existent machine learning algorithm and trained the algorithm based on their patient cohort to predict thrombolysis outcome. However, when reusing the current machine learning algorithms, there is always a trade-off between flexibility and interpretability 37 : Inflexible algorithms have a restrictive ability to estimate the boundaries between different outcome classes, therefore presenting lower predictive power.…”

Section: Introductionmentioning

confidence: 99%

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A new machine learning algorithm with high interpretability for improving the safety and efficiency of thrombolysis for stroke patients: A hospital-based pilot study

Shao

Chan

et al. 2023

DIGITAL HEALTH

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Background Thrombolysis is the first-line treatment for patients with acute ischemic stroke. Previous studies leveraged machine learning to assist neurologists in selecting patients who could benefit the most from thrombolysis. However, when designing the algorithm, most of the previous algorithms traded interpretability for predictive power, making the algorithms hard to be trusted by neurologists and be used in real clinical practice. Methods Our proposed algorithm is an advanced version of classical k-nearest neighbors classification algorithm (KNN). We achieved high interpretability by changing the isotropy in feature space of classical KNN. We leveraged a cohort of [Formula: see text] patients to prove that our algorithm maintains the interpretability of previous models while in the meantime improving the predictive power when compared with the existing algorithms. The predictive powers of models were assessed by area under the receiver operating characteristic curve (AUC). Results In terms of interpretability, only onset time, diabetes, and baseline National Institutes of Health Stroke Scale (NIHSS) were statistically significant and their contributions to the final prediction were forced to be proportional to their feature importance values by the rescaling formula we defined. In terms of predictive power, our advanced KNN (AUC 0.88) outperformed the classical KNN (AUC 0.75, [Formula: see text]). Conclusions Our preliminary results show that the advanced KNN achieved high AUC and identified consistent significant clinical features as previous clinical trials/observational studies did. This model shows the potential to assist in thrombolysis patient selection for improving the successful rate of thrombolysis.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new machine learning algorithm with high interpretability for improving the safety and efficiency of thrombolysis for stroke patients: A hospital-based pilot study

Shao

Chan

et al. 2023

DIGITAL HEALTH

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“…Some models provided both predictions on early clinical outcomes and a long-term 3-month mRS ( 40 , 41 ). A recent model in 2021 ( 42 ) predicted the final infarct volumes for patients after thrombolysis therapy. Zhu et al ( 43 ) only predicted 1-h NIHSS after thrombolysis.…”

Section: Clinical Goal Definitionmentioning

confidence: 99%

“…The more estimated parameters the model algorithm depends on, the more flexible the model is considered to be. The algorithms used by previous studies, from the most restrictive to the most flexible, were risk score ( 16 , 17 , 19 – 23 , 33 , 35 ), nomogram ( 27 , 31 , 32 , 37 – 39 ), logistic regression ( 25 , 26 , 28 , 29 , 34 , 36 , 40 , 43 , 48 ), tree-based machine learning models ( 18 , 29 , 43 , 48 ), support-vector machine (SVM) ( 18 , 24 , 29 ), and deep learning neural network ( 29 , 30 , 41 , 42 , 48 ).…”

Section: Model Algorithm Developmentmentioning

confidence: 99%

The feasibility and accuracy of machine learning in improving safety and efficiency of thrombolysis for patients with stroke: Literature review and proposed improvements

Shao

Chen

et al. 2022

Front. Neurol.

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In the treatment of ischemic stroke, timely and efficient recanalization of occluded brain arteries can successfully salvage the ischemic brain. Thrombolysis is the first-line treatment for ischemic stroke. Machine learning models have the potential to select patients who could benefit the most from thrombolysis. In this study, we identified 29 related previous machine learning models, reviewed the models on the accuracy and feasibility, and proposed corresponding improvements. Regarding accuracy, lack of long-term outcome, treatment option consideration, and advanced radiological features were found in many previous studies in terms of model conceptualization. Regarding interpretability, most of the previous models chose restrictive models for high interpretability and did not mention processing time consideration. In the future, model conceptualization could be improved based on comprehensive neurological domain knowledge and feasibility needs to be achieved by elaborate computer science algorithms to increase the interpretability of flexible algorithms and shorten the processing time of the pipeline interpreting medical images.

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“…A neural network ensemble is a learning paradigm that solves a problem by combining multiple neural networks. Ensemble learning combines predictions from many neural network models to reduce prediction variation and generalization errors [30]. It is a machine-learning technique that mixes numerous base models to create a single bestpredictive model.…”

Section: Experiments 3: Ensemble Network Architecturementioning

confidence: 99%

An Ensemble Deep Learning Network in Classifying the Early CT Slices of Ischemic Stroke Patients

Rajendran¹,

Radhakrishnan²,

Viswanathan³

2022

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The human brain is the body's most complicated organ. Constant blood flow is essential for the sustained functioning of the brain. A blocked blood vessel's interruption of blood supply prevents oxygen and nutrients to the brain tissues. This results in a life-threatening brain disease called Ischemic Stroke. Computed Tomography (CT) images are widely used in the diagnosis of Ischemic Stroke because of their faster acquisition and compatibility with most life support devices. CT acquired from the patients who arrived with stroke symptoms is Primary CT (PCT). After some hours CT taken for the same patient is Secondary CT (SCT). Stroke lesions may not be visible in PCT, whereas visible in SCT. Learning the features automatically using a Convolutional Neural Network (CNN) is essential to classify normal and abnormal CT slices. These networks are capable of learning the global features effectively for image classification. Though this CNN approach works, achieving desired accuracy was challenging. Different architectures considered for this CNN experimentation are VGG1, VGG2, VGG3, VGG16, InceptionV3, and ResNet50. This novel work provides a detailed explanation of the three experiments conducted using PCT and SCT slices. Three experiments are conducted using SCT and PCT slices. The pretrained VGG16, ResNet50, and InceptionV3 networks with the ImageNet database are applied as a first approach. Both SCT and PCT slices are used for testing alone. It resulted in 49.22%, 47.076% and 49.36% classification accuracy. In the second approach, different models were trained for classification from PCT and SCT slices. This includes the networks like VGG1, VGG2, VGG3, VGG16, VGG16 with dropout, ResNet, ResNet50 with lambda regularization, InceptionV3, and InceptionV3 with lambda regularization. The accuracies achieved are 68%, 69.4%, 72%, 78.2%, 79.1%, 77%, 77.8%, 79.6% and 80.1%. The accuracy was improved with dropout and lambda regularization. The networks with high accuracy are selected and an ensemble model is developed as a third approach. ResNet50, VGG16, and InceptionV3 are combined to form an ensemble network. This ensemble network yielded an accuracy of 81.98% when SCT and PCT slices are used for both training and testing. And produced 74% accuracy when PCT slices alone were used. Also produced 93.76% accuracy when SCT slices alone were used.

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Ensemble learning accurately predicts the potential benefits of thrombolytic therapy in acute ischemic stroke

Cited by 6 publications

References 27 publications

A new machine learning algorithm with high interpretability for improving the safety and efficiency of thrombolysis for stroke patients: A hospital-based pilot study

A new machine learning algorithm with high interpretability for improving the safety and efficiency of thrombolysis for stroke patients: A hospital-based pilot study

The feasibility and accuracy of machine learning in improving safety and efficiency of thrombolysis for patients with stroke: Literature review and proposed improvements

An Ensemble Deep Learning Network in Classifying the Early CT Slices of Ischemic Stroke Patients

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