A Stroke Risk Detection: Improving Hybrid Feature Selection Method

Zhang, Yonglai; Zhou, Yaojian; Zhang, Dongsong; Song, Wenai

doi:10.2196/12437

Cited by 28 publications

(16 citation statements)

References 58 publications

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“…For example, the Field Assessment Stroke Triage for Emergency Destination (FAST-ED) app improves the triage of patients with AIS, reduces hospital arrivals times, and maximizes the use of thrombolytic therapy [ 9 ]. Mobile technologies have been widely used to improve the management of stroke in different stages for various purposes [ 13 , 14 ]. Nevertheless, reports on the use of smartphone platforms incorporated into the overall emergency management process—from the prehospital stage to subsequent admission for further in-hospital treatment—are limited [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Use of a Smartphone Platform to Help With Emergency Management of Acute Ischemic Stroke: Observational Study

Chen

Song

et al. 2021

JMIR Mhealth Uhealth

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Background To improve the outcomes of acute ischemic stroke (AIS), timely thrombolytic therapy is crucial. Series strategies were recommended to reduce door-to-needle (DTN) time for AIS. Mobile technologies are feasible and have been used in stroke management for various purposes. However, the use of smartphone platforms that integrate series strategies through the entire first aid process to improve emergency management of AIS remains to be verified. Objective This study aims to describe the utility and application of a smartphone platform in the emergency management of AIS and report the DTN time for patients with AIS during its 2-year application period. Our results are relevant to digital health management. Methods A smartphone platform named “Green” was developed to incorporate the field assessment, hospital recommendation, prehospital notification, real-time communication, clinical records creation, key time-stamping, and quality control to streamline and standardize overall AIS emergency management processes. The emergency medical system (EMS) and all the emergency departments in Beijing have used this platform since 2018. From January 1, 2018, to December 31, 2019, 8457 patients diagnosed with AIS received intravenous tissue-type plasminogen activator therapy. The median DTN time and the proportions of patients with DTN times of ≤60 minutes and ≤45 minutes were reported. Results During the 2-year application period of this platform, the median DTN time was 45 minutes, and the proportions of patients with DTN times of ≤60 minutes and ≤45 minutes were 74.6% and 50.5%, respectively. The median DTN time was significantly reduced from 50 minutes in 2018 to 42 minutes in 2019 (P<.001). The proportions of patients with DTN times of ≤60 minutes and ≤45 minutes increased from 66.1% and 40.7%, respectively, in 2018 to 80.7% and 57.3%, respectively, in 2019 (both P<.001). Sustained improvement in DTN time was seen during all the observed months. The improvement occurred across all facilities, and the variations among hospitals also decreased. The median DTN time for patients transferred by ambulances (43 minutes) was significantly shorter than those who reached hospitals by themselves (47 minutes; P<.001). Conclusions Sustained reductions in DTN time reflected the improvement in AIS emergency management processes. The use of a smartphone platform integrating recommended strategies throughout all first aid stages is a practical way to help the emergency management of AIS.

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Section: Introductionmentioning

confidence: 99%

Use of a Smartphone Platform to Help With Emergency Management of Acute Ischemic Stroke: Observational Study

Chen

Song

et al. 2021

JMIR Mhealth Uhealth

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“…Li X et al used generalized linear model, Bayes model and decision tree model to predict the risk of ischemic stroke and other thromboembolism of people with atrial fibrillation [18]. Zhang Y et al employed a variety of filter-based feature selection models to improve the ineffective feature selection in existing research on stroke risk detection [19]. H Asadi et al applied machine learning to predict the outcome of acute ischemic stroke post intra-arterial therapy [20].…”

Section: Discussionmentioning

confidence: 99%

Using machine learning models to improve stroke risk level classification methods of China national stroke screening

Bian

et al. 2019

BMC Med Inform Decis Mak

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BackgroundWith the character of high incidence, high prevalence and high mortality, stroke has brought a heavy burden to families and society in China. In 2009, the Ministry of Health of China launched the China national stroke screening and intervention program, which screens stroke and its risk factors and conducts high-risk population interventions for people aged above 40 years old all over China. In this program, stroke risk factors include hypertension, diabetes, dyslipidemia, smoking, lack of exercise, apparently overweight and family history of stroke. People with more than two risk factors or history of stroke or transient ischemic attack (TIA) are considered as high-risk. However, it is impossible for this criterion to classify stroke risk levels for people with unknown values in fields of risk factors. The missing of stroke risk levels results in reduced efficiency of stroke interventions and inaccuracies in statistical results at the national level. In this paper, we use 2017 national stroke screening data to develop stroke risk classification models based on machine learning algorithms to improve the classification efficiency.MethodFirstly, we construct training set and test sets and process the imbalance training set based on oversampling and undersampling method. Then, we develop logistic regression model, Naïve Bayesian model, Bayesian network model, decision tree model, neural network model, random forest model, bagged decision tree model, voting model and boosting model with decision trees to classify stroke risk levels.ResultThe recall of the boosting model with decision trees is the highest (99.94%), and the precision of the model based on the random forest is highest (97.33%). Using the random forest model (recall: 98.44%), the recall will be increased by about 2.8% compared with the method currently used, and several thousands more people with high risk of stroke can be identified each year.ConclusionModels developed in this paper can improve the current screening method in the way that it can avoid the impact of unknown values, and avoid unnecessary rescreening and intervention expenditures. The national stroke screening program can choose classification models according to the practice need.

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“…As the number of variants was too large to apply deep learning models directly, to construct the features for the deep learning models, we used feature selection to reduce variant dimension (Figure 1B). Feature selection is one of the core concepts in machine learning that hugely impacts the performance of a model [32][33][34][35]. The data features that are used to train machine learning models have a huge influence on the performance that we can achieve.…”

Section: Identifying Contributory Common Genetic Variantsmentioning

confidence: 99%

Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning

Wang¹,

Avillach²

2021

JMIR Med Inform

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Background In the United States, about 3 million people have autism spectrum disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of this disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions in children with ASD. Identification of ASD based on objective pathogenic mutation screening is the major first step toward early intervention and effective treatment of affected children. Objective Recent investigation interrogated genomics data for detecting and treating autism disorders, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks perform better than shallow machine learning models on complex and high-dimensional data, in this study, we sought to apply deep learning to genetic data obtained across thousands of simplex families at risk for ASD to identify contributory mutations and to create an advanced diagnostic classifier for autism screening. Methods After preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on a chi-square test. A convolutional neural network–based diagnostic classifier was then designed using the identified significant common variants to predict autism. The performance was then compared with shallow machine learning–based classifiers and randomly selected common variants. Results The selected contributory common variants were significantly enriched in chromosome X while chromosome Y was also discriminatory in determining the identification of autistic individuals from nonautistic individuals. The ARSD, MAGEB16, and MXRA5 genes had the largest effect in the contributory variants. Thus, screening algorithms were adapted to include these common variants. The deep learning model yielded an area under the receiver operating characteristic curve of 0.955 and an accuracy of 88% for identifying autistic individuals from nonautistic individuals. Our classifier demonstrated a considerable improvement of ~13% in terms of classification accuracy compared to standard autism screening tools. Conclusions Common variants are informative for autism identification. Our findings also suggest that the deep learning process is a reliable method for distinguishing the diseased group from the control group based on the common variants of autism.

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A Stroke Risk Detection: Improving Hybrid Feature Selection Method

Cited by 28 publications

References 58 publications

Use of a Smartphone Platform to Help With Emergency Management of Acute Ischemic Stroke: Observational Study

Use of a Smartphone Platform to Help With Emergency Management of Acute Ischemic Stroke: Observational Study

Using machine learning models to improve stroke risk level classification methods of China national stroke screening

Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning

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