Automated Ischemic Stroke Subtyping Based on Machine Learning Approach

Fang, Gang; Xu, Peng; Liu, Wenbin

doi:10.1109/access.2020.3004977

Cited by 31 publications

(20 citation statements)

References 25 publications

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“…Although these clinical studies have achieved certain degree of success, additional manual work is needed to extract features to apply these research results. Recently, machine learning methods have been a powerful tool for precision medicine in stroke [17,[21][22][23][24]26]. Meanwhile, these methods are also applied to different data formats [25,[34][35][36].…”

Section: Discussionmentioning

confidence: 99%

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Interpretable CNN for ischemic stroke subtype classification with active model adaptation

Zhang

Wang

Pei

et al. 2022

BMC Med Inform Decis Mak

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Background TOAST subtype classification is important for diagnosis and research of ischemic stroke. Limited by experience of neurologist and time-consuming manual adjudication, it is a big challenge to finish TOAST classification effectively. We propose a novel active deep learning architecture to classify TOAST. Methods To simulate the diagnosis process of neurologists, we drop the valueless features by XGB algorithm and rank the remaining ones. Utilizing active learning framework, we propose a novel causal CNN, in which it combines with a mixed active selection criterion to optimize the uncertainty of samples adaptively. Meanwhile, KL-focal loss derived from the enhancement of Focal loss by KL regularization is introduced to accelerate the iterative fine-tuning of the model. Results To evaluate the proposed method, we construct a dataset which consists of totally 2310 patients. In a series of sequential experiments, we verify the effectiveness of each contribution by different evaluation metrics. Experimental results show that the proposed method achieves competitive results on each evaluation metric. In this task, the improvement of AUC is the most obvious, reaching 77.4. Conclusions We construct a backbone causal CNN to simulate the neurologist process of that could enhance the internal interpretability. The research on clinical data also indicates the potential application value of this model in stroke medicine. Future work we would consider various data types and more comprehensive patient types to achieve fully automated subtype classification.

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Section: Discussionmentioning

confidence: 99%

“…Next, we build a series of experiments, including machine learning and deep learning algorithms, as the baseline. Most of them are analyzed in these related work [21][22][23][24]. Meanwhile, we compare some related and advanced deep learning algorithms [25].…”

Section: Experiments Setupmentioning

confidence: 99%

Interpretable CNN for ischemic stroke subtype classification with active model adaptation

Zhang

Wang

Pei

et al. 2022

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

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“…Previous studies applying NLP and machine learning to classify stroke into subtypes have focused on automating ischemic stroke subtyping into specific sub-categories using the EMR [ 30 , 31 ] or a selection of available features [ 32 ]. Others, such as the Edinburgh Information Extraction for Radiology reports (EdIE-R) [ 33 ] have shown good performance of text mining systems in subtyping already expert-validated stroke cases into the three main subtypes (IS, ICH and SAH) based on radiology scan reports.…”

Section: Discussionmentioning

confidence: 99%

Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke

Rannikmäe¹,

Tominey

et al. 2021

BMC Med Inform Decis Mak

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Background Better phenotyping of routinely collected coded data would be useful for research and health improvement. For example, the precision of coded data for hemorrhagic stroke (intracerebral hemorrhage [ICH] and subarachnoid hemorrhage [SAH]) may be as poor as < 50%. This work aimed to investigate the feasibility and added value of automated methods applied to clinical radiology reports to improve stroke subtyping. Methods From a sub-population of 17,249 Scottish UK Biobank participants, we ascertained those with an incident stroke code in hospital, death record or primary care administrative data by September 2015, and ≥ 1 clinical brain scan report. We used a combination of natural language processing and clinical knowledge inference on brain scan reports to assign a stroke subtype (ischemic vs ICH vs SAH) for each participant and assessed performance by precision and recall at entity and patient levels. Results Of 225 participants with an incident stroke code, 207 had a relevant brain scan report and were included in this study. Entity level precision and recall ranged from 78 to 100%. Automated methods showed precision and recall at patient level that were very good for ICH (both 89%), good for SAH (both 82%), but, as expected, lower for ischemic stroke (73%, and 64%, respectively), suggesting coded data remains the preferred method for identifying the latter stroke subtype. Conclusions Our automated method applied to radiology reports provides a feasible, scalable and accurate solution to improve disease subtyping when used in conjunction with administrative coded health data. Future research should validate these findings in a different population setting.

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“…Moreover, it should be noted that most researchers in previous studies aimed to apply the feature selection techniques for not only higher accuracy but also an improvement in understanding the causes of NCDs. The NCDs predictive results of previous studies implied that DNN, SVM, Ensemble classifiers achieved the best performances when compared with other baseline models [28][29].…”

Section: A Machine Learning Techniques For Non-communicable Diseasesmentioning

confidence: 94%

“…Various studies have focused on the accuracy enhancement of NCDs diagnostic models concerning feature selection techniques and refined machine-learning classifiers [18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: A Machine Learning Techniques For Non-communicable Diseasesmentioning

confidence: 99%

Explainable Artificial Intelligence Based Framework for Non-Communicable Diseases Prediction

et al. 2021

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The rapid rise of non-communicable diseases (NCDs) becomes one of the serious health issues and the leading cause of death worldwide. In recent years, artificial intelligence-based systems have been developed to assist clinicians in decision-making to reduce morbidity and mortality. However, a common drawback of these modern studies is related to explanations of their output. In other words, understanding the inner logic behind the predictions is hidden to the end-user. Thus, clinicians struggle to interpret these models because of their black-box nature, and hence they are not acceptable in the medical practice. To address this problem, we have proposed a Deep Shapley Additive Explanations (DeepSHAP) based deep neural network framework equipped with a feature selection technique for NCDs prediction and explanation among the population in the United States. Our proposed framework comprises three components: First, representative features are done based on the elastic net-based embedded feature selection technique; second a deep neural network classifier is tuned with the hyper-parameters and used to train the model with the selected feature subset; third, two kinds of model explanation are provided by the DeepSHAP approach. Herein, (I) explaining the risk factors that affected the model's prediction from the population-based perspective; (II) aiming to explain a single instance from the human-centered perspective. The experimental results indicated that the proposed model outperforms various state-of-the-art models. In addition, the proposed model can improve the medical understanding of NCDs diagnosis by providing general insights into the changes in disease risk at the global and local levels. Consequently, DeepSHAP based explainable deep learning framework contributes not only to the medical decision support systems but also can provide to real-world needs in other domains.

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Automated Ischemic Stroke Subtyping Based on Machine Learning Approach

Cited by 31 publications

References 25 publications

Interpretable CNN for ischemic stroke subtype classification with active model adaptation

Interpretable CNN for ischemic stroke subtype classification with active model adaptation

Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke

Explainable Artificial Intelligence Based Framework for Non-Communicable Diseases Prediction

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