A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data

Awal, Md. Abdul; Masud, Mehedi; Hossain, Md. Shahadat; Bulbul, Abdullah Al-Mamun; Mahmud, Sakib; Bairagi, Anupam Kumar

doi:10.1109/access.2021.3050852

Cited by 64 publications

(67 citation statements)

References 47 publications

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“…To identify the most commonly mentioned subjects in a large tweet sample, they created a latent Dirichlet allocation (LDA) model. Sentiment analysis was also performed to gain an idea of the overall feelings and emotions in Australia related to COVID-19.This research [ 15 ] proposed a machine learning framework based on Bayesian optimization to detect COVID-19 and solve related issues from a clinical perspective. An optimization approach [ 16 ] considering individuals' isolation and social distancing characteristics was developed.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

Alam

Khan

Dhruba

et al. 2021

Computational and Mathematical Methods in Medicine

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The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people’s feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people’s minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public’s opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

Alam

Khan

Dhruba

et al. 2021

Computational and Mathematical Methods in Medicine

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“…To overcome this problem, we employed the parameter (weight) fusion model concept in this study. Besides, some other works focused on explainable AI [33][34][35], metalearning [36], and segmentation [37] based frameworks for COVID-19 and pneumonia-related healthcare systems.…”

Section: Related Literaturementioning

confidence: 99%

Artificial Neural Network-Based Deep Learning Model for COVID-19 Patient Detection Using X-Ray Chest Images

Masud

Alhumyani

Anand

et al. 2021

Journal of Healthcare Engineering

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The world is experiencing an unprecedented crisis due to the coronavirus disease (COVID-19) outbreak that has affected nearly 216 countries and territories across the globe. Since the pandemic outbreak, there is a growing interest in computational model-based diagnostic technologies to support the screening and diagnosis of COVID-19 cases using medical imaging such as chest X-ray (CXR) scans. It is discovered in initial studies that patients infected with COVID-19 show abnormalities in their CXR images that represent specific radiological patterns. Still, detection of these patterns is challenging and time-consuming even for skilled radiologists. In this study, we propose a novel convolutional neural network- (CNN-) based deep learning fusion framework using the transfer learning concept where parameters (weights) from different models are combined into a single model to extract features from images which are then fed to a custom classifier for prediction. We use gradient-weighted class activation mapping to visualize the infected areas of CXR images. Furthermore, we provide feature representation through visualization to gain a deeper understanding of the class separability of the studied models with respect to COVID-19 detection. Cross-validation studies are used to assess the performance of the proposed models using open-access datasets containing healthy and both COVID-19 and other pneumonia infected CXR images. Evaluation results show that the best performing fusion model can attain a classification accuracy of 95.49% with a high level of sensitivity and specificity.

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“…The pandemic is evolving and with each day new data are generated for health monitoring or diagnosis purposes [22]. In [10] the optimized a machine learning-based framework using inpatient's facility data was presented, that will give a user-friendly, cost-effective, and time-efficient solution to this pandemic.…”

Section: ) Big Data Solutionsmentioning

confidence: 99%

Two-Strain COVID-19 Model Using Delayed Dynamic System and Big Data

Martsenyuk¹,

Bernaś²,

Kłos-Witkowska³

2021

IEEE Access

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The proposed model is based on COVID-19 Big Data Hub. It enables us to predict pandemics development taking into account multiple virus strains and delays of infectiousness. Two-strain dynamic models with distributed delays have been fitted to the time series retreived from COVID data hub. The data at the national, regional, and city-level which are seamlessly integrated with World Bank Open Data, Google Mobility Reports, Apple Mobility Reports, have been used. The parameter identification has been fulfilled with the help of COBYLA algorithm. The simulations have been implemented with the help of Julia highperformance computing. The effect of the time delays is analyzed. The considered pipeline utilizes the data from the Hub to generate the COVID model and to produce a reliable prediction.

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A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data

Cited by 64 publications

References 47 publications

Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data

Artificial Neural Network-Based Deep Learning Model for COVID-19 Patient Detection Using X-Ray Chest Images

Two-Strain COVID-19 Model Using Delayed Dynamic System and Big Data

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