Cardiac Magnetic Resonance Imaging (CMRI) Applications in Patients with Chest Pain in the Emergency Department: A Narrative Review

Zareiamand, Hossein; Darroudi, Amin; Mohammadi, Iraj; Moravvej, Seyed Vahid; Danaei, Saba; Alizadehsani, Roohallah

doi:10.3390/diagnostics13162667

Cited by 15 publications

(6 citation statements)

References 46 publications

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“…One advantage of LSTM networks is their proficiency in handling long-term dependencies, enabling them to capture subtle relationships and trends that span extended periods. By analyzing historical price data, trading volumes, and other relevant factors, LSTM networks can identify recurring patterns, seasonal trends, and market cycles that traditional models might miss [52]. Another strength of LSTM networks is their capacity to process and retain information over long sequences.…”

Section: Sm Forecastingmentioning

confidence: 99%

Integrating Social Media Data and Historical Stock Prices for Predictive Analysis: A Reinforcement Learning Approach

Li,

Zhang

2023

IJACSA

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The reliance on data collection for assessing individual behavior and actions has intensified, particularly with the proliferation of digital platforms. People often use the Internet to express their opinions and experiences about various products and services on social media and personal websites. Concurrently, the stock market, a key driver of commercial and industrial growth, has seen a surge in research focused on predicting market trends. The vast array of information on social media regarding public sentiment towards current events, coupled with the known impact of financial news on stock prices, has led to the application of data mining techniques for understanding market volatility. This research proposes a novel method that integrates social media data, encompassing public sentiment, news, and historical stock prices, to predict future stock trends. The approach involves two primary phases. The first phase develops a sentiment analysis (SA) model using three dilated convolution layers for feature extraction and classification. Addressing the challenge of unbalanced classification, a reinforcement learning (RL)-based strategy is employed, wherein an agent receives varied rewards for accurate classification, with a bias towards the minority class. Additionally, a unique clustering-based mutation operator within a differential equation (DE) framework is introduced to initiate the backpropagation (BP) process. The second phase incorporates an attention-based long short-term memory (LSTM) model, merging historical stock prices with sentiment data. An experimental analysis of the study dataset is conducted to determine optimal values for significant parameters, including the reward function.

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Section: Sm Forecastingmentioning

confidence: 99%

Integrating Social Media Data and Historical Stock Prices for Predictive Analysis: A Reinforcement Learning Approach

Li,

Zhang

2023

IJACSA

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“…Several deep learning-based methods have been developed for tackling the issue of imbalanced datasets (Dablain et al 2023, Taherinavid et al 2023; however, they often need help with significant obstacles (Wang et al 2022, Zareiamand et al 2023. These methods typically depend on extensive, well-distributed datasets to achieve optimal results-a condition seldom met in real-life situations such as myocarditis prediction, where the prevalence of positive cases is substantially lower than negative ones.…”

Section: Introductionmentioning

confidence: 99%

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration

Mirzaee Moghaddam Kasmaee,

Ataei,

Moravvej

et al. 2024

Physiol. Meas.

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Objective. Myocarditis poses a significant health risk, often precipitated by viral infections like Coronavirus disease (COVID-19), and can lead to fatal cardiac complications. As a less invasive alternative to the standard diagnostic practice of endomyocardial biopsy, which is highly invasive and thus limited to severe cases, Cardiac Magnetic Resonance (CMR) imaging offers a promising solution for detecting myocardial abnormalities. Approach. This study introduces a deep model called ELRL-MD that combines ensemble learning and Reinforcement Learning (RL) for effective myocarditis diagnosis from CMR images. The model begins with pre-training via the Artificial Bee Colony (ABC) algorithm to enhance the starting point for learning. An array of Convolutional Neural Networks (CNNs) then works in concert to extract and integrate features from CMR images for accurate diagnosis. Leveraging the Z-Alizadeh Sani myocarditis CMR dataset, the model employs RL to navigate the dataset's imbalance by conceptualizing diagnosis as a decision-making process. Main Results. ELRL-DM demonstrates remarkable efficacy, surpassing other deep learning, conventional machine learning, and transfer learning models, achieving an F-measure of 88.2\% and a geometric mean of 90.6\%. Extensive experimentation helped pinpoint the optimal reward function settings and the perfect count of CNNs. Significance. The study addresses the primary technical challenge of inherent data imbalance in CMR imaging datasets and the risk of models converging on local optima due to suboptimal initial weight settings. Further analysis, leaving out ABC and RL components, confirmed their contributions to the model's overall performance, underscoring the effectiveness of addressing these critical technical challenges.

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“…Because of adaptive changes brought about by long-term intensive training, which can manifest as changes in heart rate, rhythm, and morphology, it is challenging to differentiate between pathological and physiological patterns on the ECG (Sokunbi et al 2021, Zareiamand et al 2023. Manual ECG interpretation by physicians is the standard of care but can be costly, time-intensive and may not be easily accessible.…”

Section: Introductionmentioning

confidence: 99%

MLP-RL-CRD: diagnosis of cardiovascular risk in athletes using a reinforcement learning-based multilayer perceptron

Bostani,

Mirzaeibonehkhater,

Najafi

et al. 2023

Physiol. Meas.

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Objective. Pre-participation medical screening of athletes is necessary to pinpoint individuals susceptible to cardiovascular events. Approach. The article presents a reinforcement learning (RL)-based multilayer perceptron, termed MLP-RL-CRD, designed to detect cardiovascular risk among athletes. The model underwent training using a publicized dataset that included the anthropological measurements (such as height and weight) and biomedical metrics (covering blood pressure and pulse rate) of 26 002 athletes. To address the data imbalance, a novel RL-based technique was adopted. The problem was framed as a series of sequential decisions in which an agent classified a received instance and received a reward at each level. To resolve the insensitivity to the initialization of conventional gradient-based learning methods, a mutual learning-based artificial bee colony (ML-ABC) was proposed. Main Results. The model outcomes were validated against positive (P) and negative (N) ECG findings that had been labeled by experts to signify individuals ‘at risk’ and ‘not at risk,’ respectively. The MLP-RL-CRD approach achieves superior outcomes (F-measure 87.4%; geometric mean 89.6%) compared with other deep models and traditional machine learning techniques. Optimal values for crucial parameters, including the reward function, were identified for the model based on experiments on the study dataset. Ablation studies, which omitted elements of the suggested model, affirmed the autonomous, positive, stepwise influence of these components on performing the model. Significance. This study introduces a novel, effective method for early cardiovascular risk detection in athletes, merging reinforcement learning and multilayer perceptrons, advancing medical screening and predictive healthcare. The results could have far-reaching implications for athlete health management and the broader field of predictive healthcare analytics.

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Cardiac Magnetic Resonance Imaging (CMRI) Applications in Patients with Chest Pain in the Emergency Department: A Narrative Review

Cited by 15 publications

References 46 publications

Integrating Social Media Data and Historical Stock Prices for Predictive Analysis: A Reinforcement Learning Approach

Integrating Social Media Data and Historical Stock Prices for Predictive Analysis: A Reinforcement Learning Approach

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration

MLP-RL-CRD: diagnosis of cardiovascular risk in athletes using a reinforcement learning-based multilayer perceptron

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