Long Short-Term Memory Recurrent Neural Network for Stroke Prediction

Chantamit-o-pas, Pattanapong; Goyal, Madhu

doi:10.1007/978-3-319-96136-1_25

Cited by 31 publications

(18 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We first ran the multinomial logistic (MNL) regression to predict the PEFR risk category for a specific time period for each cluster using only the concurrent PM measurements. We then ran the Long Short-Term Memory (LSTM) deep learning network [31], [32] to predict the same targets and compare the results with those from the conventional regression approach (MNL).…”

Section: Deep Learning Algorithm and Predictionmentioning

confidence: 99%

Predicting Asthma Attacks: Effects of Indoor PM Concentrations on Peak Expiratory Flow Rates of Asthmatic Children

et al. 2020

View full text Add to dashboard Cite

Despite ample research on the association between indoor air pollution and allergic disease prevalence, public health and environmental policies still lack predictive evidence for developing a preventive guideline for patients or vulnerable populations mostly due to limitation of real-time big data and model predictability. Recent popularity of IoT and machine learning techniques could provide enabling technologies for collecting real-time big data and analyzing them for more accurate prediction of allergic disease risks for evidence-based intervention, but the effort is still in its infancy. This pilot study explored and evaluated the feasibility of a deep learning algorithm for predicting asthma risk. It is based on peak expiratory flow rates (PEFR) of 14 pediatric asthma patients visiting the Korea University Medical Center and indoor particulate matter PM10 and PM2.5 concentration data collected at their residence every 10 minutes using a PM monitoring device with a low-cost sensor between September 1, 2017 and August 31, 2018. We interpolated the PEFR results collected twice a day for each patient throughout the day so that it can be matched to the PM and other weather data. The PEFR results were classified into three categories such as 'Green' (normal), 'Yellow' (mild to moderate exacerbation) and 'Red' (severe exacerbation) with reference to their best peak flow value. Long Short-Term Memory (LSTM) model was trained using the first 10 months of the linked data and predicted asthma risk categories for the next 2 months during the study period. LSTM model is found to predict the asthma risk categories better than multinomial logistic (MNL) regression as it incorporates the cumulative effects of PM concentrations over time. Upon successful modifications of the algorithm based on a larger sample, this approach could potentially play a groundbreaking role for the scientific data-driven medical decision making. INDEX TERMS Asthma, indoor particulate matter, deep learning, peak expiratory flow rates, real-time monitoring.

show abstract

Section: Deep Learning Algorithm and Predictionmentioning

confidence: 99%

Predicting Asthma Attacks: Effects of Indoor PM Concentrations on Peak Expiratory Flow Rates of Asthmatic Children

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The cells of LSTM learn to recognize important inputs (input gate), learn to preserve them for a long period of state storage and defined time, and execute learning to extract them whenever necessary [43,44]. In a recent research trend related to LSTMs, studies have attempted to analyze the risk factors of EHRs (Electronic Healthcare Records) to predict LSTM-based cerebrovascular diseases [45]. Specifically, by incorporating ICD-10 codes [46] and other potential risk factors patterns from EHRs, Chantamit [45] confirmed that the LSTM algorithm is the most suitable for predictive analysis of any cerebrovascular disease or stroke.…”

Section: Stroke Prediction Using Machine Learning and Deep Learningmentioning

confidence: 99%

“…In a recent research trend related to LSTMs, studies have attempted to analyze the risk factors of EHRs (Electronic Healthcare Records) to predict LSTM-based cerebrovascular diseases [45]. Specifically, by incorporating ICD-10 codes [46] and other potential risk factors patterns from EHRs, Chantamit [45] confirmed that the LSTM algorithm is the most suitable for predictive analysis of any cerebrovascular disease or stroke. In another study, a methodology based on the LSTM model for predicting HDM (hemorrhagic transformation) in ischemic stroke was proposed by Yu et al [47].…”

Section: Stroke Prediction Using Machine Learning and Deep Learningmentioning

confidence: 99%

“…This LSTM consists of a structure that explicitly transfers past information to the next state, and by learning long-term dependencies, the cell state serves to convey past information to the next step [41], [42][43][44]. Deep Learning's LSTM has overcome the structural shortcomings of the existing RNN and can solve the problem of having a vanishing gradient when error values are propagated to the neural network layer [41,42,45,47]. LSTM consists of a cell state, an input gate, a forget gate, and an output gate, and vector output values at each gate are generated via the sigmoid layer and the tanh layer (see Figure 5).…”

Section: Experiments and Analysis Based On Deep Learningmentioning

confidence: 99%

See 1 more Smart Citation

AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals

Park

Kwon

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

Stroke is a leading cause of disabilities in adults and the elderly which can result in numerous social or economic difficulties. If left untreated, stroke can lead to death. In most cases, patients with stroke have been observed to have abnormal bio-signals (i.e., ECG). Therefore, if individuals are monitored and have their bio-signals measured and accurately assessed in real-time, they can receive appropriate treatment quickly. However, most diagnosis and prediction systems for stroke are image analysis tools such as CT or MRI, which are expensive and difficult to use for real-time diagnosis. In this paper, we developed a stroke prediction system that detects stroke using real-time bio-signals with artificial intelligence (AI). Both machine learning (Random Forest) and deep learning (Long Short-Term Memory) algorithms were used in our system. EMG (Electromyography) bio-signals were collected in real time from thighs and calves, after which the important features were extracted, and prediction models were developed based on everyday activities. Prediction accuracies of 90.38% for Random Forest and of 98.958% for LSTM were obtained for our proposed system. This system can be considered an alternative, low-cost, real-time diagnosis system that can obtain accurate stroke prediction and can potentially be used for other diseases such as heart disease.

show abstract

“…They found that two HRV parameters, CV% (coefficient of variance of NN intervals) and power law slope, emerged as significantly associated with incident stroke when added to a validated clinical risk score. Chantamit-o-pas et al integrated the icd-10 code into the health records and other potential risk factors in Electronic Healthcare Records (EHRs) into the patterns and models to predict stroke [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

The Probability of Ischaemic Stroke Prediction with a Multi-Neural-Network Model

Liu

Yin

Yang

2020

Sensors

View full text Add to dashboard Cite

As is known, cerebral stroke has become one of the main diseases endangering people’s health; ischaemic strokes accounts for approximately 85% of cerebral strokes. According to research, early prediction and prevention can effectively reduce the incidence rate of the disease. However, it is difficult to predict the ischaemic stroke because the data related to the disease are multi-modal. To achieve high accuracy of prediction and combine the stroke risk predictors obtained by previous researchers, a method for predicting the probability of stroke occurrence based on a multi-model fusion convolutional neural network structure is proposed. In such a way, the accuracy of ischaemic stroke prediction is improved by processing multi-modal data through multiple end-to-end neural networks. In this method, the feature extraction of structured data (age, gender, history of hypertension, etc.) and streaming data (heart rate, blood pressure, etc.) based on a convolutional neural network is first realized. A neural network model for feature fusion is then constructed to realize the feature fusion of structured data and streaming data. Finally, a predictive model for predicting the probability of stroke is obtained by training. As shown in the experimental results, the accuracy of ischaemic stroke prediction reached 98.53%. Such a high prediction accuracy will be helpful for preventing the occurrence of stroke.

show abstract

Long Short-Term Memory Recurrent Neural Network for Stroke Prediction

Cited by 31 publications

References 19 publications

Predicting Asthma Attacks: Effects of Indoor PM Concentrations on Peak Expiratory Flow Rates of Asthmatic Children

Predicting Asthma Attacks: Effects of Indoor PM Concentrations on Peak Expiratory Flow Rates of Asthmatic Children

AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals

The Probability of Ischaemic Stroke Prediction with a Multi-Neural-Network Model

Contact Info

Product

Resources

About