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A Novel Bayesian Optimization-based Machine Learning Framework for COVID-19 Detection from Inpatient Facility Data

Md Abdul Awal, Mehedi Masud, Md Shahadat Hossain, Abdullah Al-Mamun Bulbul, S. M. Hasan Mahmud, Anupam Kumar Bairagi
2021 IEEE Access  
We have designed and optimized a machine learning-based framework using inpatient's facility data that will give a user-friendly, cost-effective, and time-efficient solution to this pandemic.  ...  The proposed framework uses Bayesian optimization to optimize the hyperparameters of the classifier and ADAptive SYNthetic (ADASYN) algorithm to balance the COVID and non-COVID classes of the dataset.  ...  Therefore, the patient needs not to visit the clinic to test the COVID-19. • We have designed a machine learning framework using Bayesian optimization adapted by the ADASYN algorithm to detect COVID-19  ... 
doi:10.1109/access.2021.3050852 pmid:34786301 pmcid:PMC8545233 fatcat:kb6q5zva55bjpdhtileexp3fp4

Data-Driven Prediction for COVID-19 Severity in Hospitalized Patients

Abdulrahman A. Alrajhi, Osama A. Alswailem, Ghassan Wali, Khalid Alnafee, Sarah AlGhamdi, Jhan Alarifi, Sarab AlMuhaideb, Hisham ElMoaqet, Ahmad AbuSalah
2022 International Journal of Environmental Research and Public Health  
The data were collected, prepared, and leveraged by a panel of clinical and data experts to develop a multi-class data-driven framework to predict severity levels for COVID-19 infections at admission time  ...  Unlike many previous studies for a similar population in the region, this study evaluated 4 machine learning models using a large training data set of 1386 patients collected between March 2020 and April  ...  In this paper, we develop and deploy a novel data-driven prognosis framework to provide patient-level predictions for the severity of COVID-19 infection for patients at the time of hospital admission.  ... 
doi:10.3390/ijerph19052958 pmid:35270653 pmcid:PMC8910504 fatcat:w7abllhimrbptogu6bo4gpoio4

Automatic extraction of knowledge for diagnosing COVID-19 disease based on text mining techniques: A systematic review

Amir Yasseen Mahdi, Siti Sophiayati Yuhaniz
2021 Periodicals of Engineering and Natural Sciences (PEN)  
Supervised machine learning has been explored as an active method for diagnosing COVID-19 disease.  ...  The main aim of the current systematic review was to highlight the gaps and challenges within the academic literature of the disease COVID-19, which included the characteristics of the data, machine learning  ...  medical texts using NLP and machine learning methods in the detection and identification of COVID-19 disease.  ... 
doi:10.21533/pen.v9i2.1945 fatcat:w7n4txikyffptia6fpngtxwjce

Forecasting the Spread of Covid-19 Under Control Scenarios Using LSTM and Dynamic Behavioral Models [article]

Seid Miad Zandavi, Taha Hossein Rashidi, Fatemeh Vafaee
2020 arXiv   pre-print
To accurately predict the regional spread of Covid-19 infection, this study proposes a novel hybrid model which combines a Long short-term memory (LSTM) artificial recurrent neural network with dynamic  ...  The parameters of the hybrid models were optimized using a genetic algorithm for each country to improve the prediction power while considering regional properties.  ...  The results show that the hybrid model can accurately predict the spread of Covid-19 based on real data.  ... 
arXiv:2005.12270v1 fatcat:7rumokxzo5awdla5fvlkfxkmga

Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

Ania Syrowatka, Masha Kuznetsova, Ava Alsubai, Adam L. Beckman, Paul A. Bain, Kelly Jean Thomas Craig, Jianying Hu, Gretchen Purcell Jackson, Kyu Rhee, David W. Bates
2021 npj Digital Medicine  
Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic.  ...  The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches.  ...  Zoe Co for assistance with data abstraction. A.S. is supported by a Fellowship Award from the Canadian Institutes of Health Research.  ... 
doi:10.1038/s41746-021-00459-8 pmid:34112939 fatcat:itib6lu3cbdwvlwbkejpm3fusu

Machine learning in patient flow: a review

Rasheed El-Bouri, Thomas Taylor, Alexey Youssef, Tingting Zhu, David A Clifton
2021 Progress in Biomedical Engineering  
We conclude that machine learning for the improvement of patient flow is still a young field with very few papers tailor-making machine learning methods for the problem being considered.  ...  This work is a review of the ways in which machine learning has been used in order to plan, improve or aid the problem of moving patients through healthcare services.  ...  For example, one of the barriers to developing effective machine learning tools for COVID-19 patients is the lack of data on COVID-19 patients.  ... 
doi:10.1088/2516-1091/abddc5 pmid:34738074 pmcid:PMC8559147 fatcat:ra4dfti3abaohifhxbhrdw6h6u

The use of artificial intelligence systems in diagnosis of pneumonia via signs and symptoms: A systematic review

Katy Stokes, Rossana Castaldo, Carlo Federici, Silvio Pagliara, Alessia Maccaro, Francesco Cappuccio, Giuseppe Fico, Marco Salvatore, Monica Franzese, Leandro Pecchia
2022 Biomedical Signal Processing and Control  
This systematic literature review aimed to investigate performance and reporting of diagnostic AI systems using machine learning (ML) for pneumonia detection based on symptoms and signs, and to provide  ...  There is strong evidence to support further investigations of ML to automatically detect pneumonia based on easily recognisable symptoms and signs.  ...  With the dramatically fast spread of COVID-19, analysing complex medical datasets based on machine learning can provide opportunities for developing a simple and efficient COVID-19 diagnostic system.  ... 
doi:10.1016/j.bspc.2021.103325 fatcat:hsj3wimob5dlrd4wxd7uqjykpa

Program

2022 2022 International Conference on Decision Aid Sciences and Applications (DASA)  
The selected features from EEG recordings of 23 subjects (AD-12 and NC-11) are used to train and test the Leastsquare support vector machine (LS-SVM) classifier with three different kernel functions.  ...  A maximum of 92.90% classification accuracy is obtained using the features of IMF-4 with 10-fold cross-validation. The results conclude that the proposed method can detect AD patients efficiently.  ...  Federated Learning is a framework addressing this problem. It consists of learning from a decentralized corpus of non-independent and identically distributed data.  ... 
doi:10.1109/dasa54658.2022.9765271 fatcat:ttqppf4j3navnaxe653mrzmezi

Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques

Aldonso Becerra-Sánchez, Armando Rodarte-Rodríguez, Nivia I. Escalante-García, José E. Olvera-González, José I. de la de la Rosa-Vargas, Gustavo Zepeda-Valles, Emmanuel de J. Velásquez-Martínez
2022 Diagnostics  
The models use risk factors as variables to predict the mortality of patients from COVID-19.  ...  This paper aims to propose an alternative algorithmic analysis for predicting the health status of patients infected with COVID-19 in Mexico.  ...  Data Availability Statement: Not applicable.  ... 
doi:10.3390/diagnostics12061396 pmid:35741207 pmcid:PMC9222115 fatcat:bocrwu2lfjfbrivq3bx2mjvz2u

Influential Usage of Big Data and Artificial Intelligence in Healthcare

Yan Cheng Yang, Saad Ul Islam, Asra Noor, Sadia Khan, Waseem Afsar, Shah Nazir
2021 Computational and Mathematical Methods in Medicine  
Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at  ...  For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy.  ...  [19] have surveyed sensor data and machine learning about mental health monitoring systems.  ... 
doi:10.1155/2021/5812499 pmid:34527076 pmcid:PMC8437645 fatcat:lmpwildgefggrisquu6ebwu2iu

A Review of the Potential of Artificial Intelligence Approaches to Forecasting COVID-19 Spreading

Mohammad Behdad Jamshidi, Sobhan Roshani, Jakub Talla, Ali Lalbakhsh, Zdeněk Peroutka, Saeed Roshani, Fariborz Parandin, Zahra Malek, Fatemeh Daneshfar, Hamid Reza Niazkar, Saeedeh Lotfi, Asal Sabet (+4 others)
2022 AI  
This short review aims to summarize and evaluate the trustworthiness and performance of some important AI-empowered approaches used for the prediction of the spread of COVID-19.  ...  The focus of such methods is on deep learning (DL) and machine learning (ML), and they can forecast nonlinear trends in epidemiological issues appropriately.  ...  The data collected by this method were then utilized as input for different forecasting models based on machine learning models (SVM, and MLP) and statistical models (Logistic Regression, LR).  ... 
doi:10.3390/ai3020028 fatcat:nftizbifxbaizdiyoqlfznq3am

Artificial intelligence-enhanced electrocardiography in cardiovascular disease management

Konstantinos C. Siontis, Peter A. Noseworthy, Zachi I. Attia, Paul A. Friedman
2021 Nature Reviews Cardiology  
Large sets of digital ECGs linked to rich clinical data have been used to develop AI models for the detection of left ventricular dysfunction, silent (previously undocumented and asymptomatic) atrial fibrillation  ...  can be detected by multilayer AI networks with precision, making the ECG a powerful, non-invasive biomarker.  ...  The COVID-19 pandemic has highlighted the need for rapid, point-of-care diagnostic testing.  ... 
doi:10.1038/s41569-020-00503-2 pmid:33526938 pmcid:PMC7848866 fatcat:jc2b2lb7qjcs3nnk5c5t5t2gry

A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection

Ravita Chahar, Ashutosh Kumar Dubey, Sushil Kumar Narang
2021 International Journal of Advanced Technology and Engineering Exploration  
An increase of 11% from the previous year was noted in the US alone due to the COVID-19 pandemic [6, 7] . The best way to detect depression is to analyze the behavior of the person.  ...  A WHO report published a decade ago stated that the number of deaths from suicide was 1 million per year [5] .  ...  [88] performed a study on patients of COVID-19.  ... 
doi:10.19101/ijatee.2021.874198 fatcat:bhrusmlbdrdvfkcnob4ueowaam

CARS 2021: Computer Assisted Radiology and Surgery Proceedings of the 35th International Congress and Exhibition Munich, Germany, June 21–25, 2021

2021 International Journal of Computer Assisted Radiology and Surgery  
Acknowledgements This work has been funded by the research project PI18/00169 from Instituto de Salud Carlos III & FEDER funds.  ...  In this paper, we present a new approach for COVID-19 detection from chest X-ray images using Deep Learning algorithms.  ...  inpatient facilities are facing.  ... 
doi:10.1007/s11548-021-02375-4 pmid:34085172 fatcat:6d564hsv2fbybkhw4wvc7uuxcy

D3.15- 3rd periodic report on JRPs [article]

Sciensano
2021 Zenodo  
We produced a framework for the use of machine-learning methods in AMR risk assessment in order to identify risk factors from high-dimensional data with more variables than data points and/or categorical  ...  .  We produced a framework for the use machine-learning methods in AMR risk assessment in order to identify risk factors from high-dimensional data with more variables than data points and/or categorical  ...  Therefore, knowledge gaps in information from T2.1 is being analysed. JRP21-WP5-T3: Machine learning approaches (M47-52) This task has not started yet.  ... 
doi:10.5281/zenodo.4897319 fatcat:dtppotwzlrhzzbzrmm2lvpvqbi
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