Machine Learning Approaches for COVID-19 Pandemic (original) (raw)
Related papers
Machine learning techniques as an efficient alternative diagnostic tool for COVID-19 cases
Signa Vitae, 2021
Background: The SARS-CoV-2 virus has demonstrated the weakness of many health systems worldwide, creating a saturation and lack of access to treatments. A bottleneck to fight this pandemic relates to the lack of diagnostic infrastructure for early detection of positive cases, particularly in rural and impoverished areas of developing countries. In this context, less costly and fast machine learning (ML) diagnosis-based systems are helpful. However, most of the research has focused on deep-learning techniques for diagnosis, which are computationally and technologically expensive. ML models have been mainly used as a benchmark and are not entirely explored in the existing literature on the topic of this paper. Objective: To analyze the capabilities of ML techniques (compared to deep learning) to diagnose COVID-19 cases based on X-ray images, assessing the performance of these techniques and using their predictive power for such a diagnosis. Methods: A factorial experiment was designed...
Machine learning is the key to diagnose COVID‑19: a proof‑of‑concept study, 2020
The reverse transcription-polymerase chain reaction (RT-PCR) assay is the accepted standard for coronavirus disease 2019 (COVID-19) diagnosis. As any test, RT-PCR provides false negative results that can be recti ed by clinicians by confronting clinical, biological and imaging data. The combination of RT-PCR and chest-CT could improve diagnosis performance, but this would requires considerable resources for its rapid use in all patients with suspected COVID-19. The potential contribution of machine learning in this situation has not been fully evaluated. The objective of this study was to develop and evaluate machine learning models using routine clinical and laboratory data to improve the performance of RT-PCR and chest-CT for diagnose COVID-19 among post-emergency hospitalized patients. All adults admitted to the ED for suspected COVID-19, and then hospitalized at Rennes academic hospital, France, between March 20, 2020 and May 5, 2020 were included in the study. Three model types were created: logistic regression, random forest, and neural network. Each model was trained to diagnose COVID-19 using different sets of variables. Area under the receiving operator characteristics curve (AUC) was the primary outcome to evaluate model's performances. 536 patients were included in the study: 106 in the COVID group, 430 in the NOT-COVID group. The AUC values of chest-CT and RT-PCR increased from 0.778 to 0.892 and from 0.852 to 0.930, respectively, with the contribution of machine learning. After generalization, machine learning models will allow increasing chest-CT and RT-PCR performances for COVID-19 diagnosis.
Machine learning Helps in Quickly Diagnosis Cases of "New Corona"
Mesopotamian Journal of Artificial Intelligence in Healthcare, 2024
Machine learning is considered one of the most significant techniques that play a vital role in diagnosing the Coronavirus. It is a set of advanced algorithms capable of analyzing medical data and identifying patterns and behaviors of diseases. It is used to interpret medical images, giving details of each image with high accuracy and efficiency, such as chest X-ray images. These algorithms are trained on a large set of images to recognise patterns that indicate the presence of infection with the Coronavirus (COVID-19). This article will provide a brief overview of the importance of machine learning in diagnosing COVID-19 by processing and analysing medical image data and helping physicians and healthcare workers provide distinguished and influential care for patients infected with this virus.
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021
The recent outbreak of the respiratory ailment COVID-19 caused by novel corona virus SARS- Cov2 is a severe and urgent global concern. In the absence of vaccine, and also treatment of COVID- 19 WHO (World Health Organization) had informed that Social distancing is the only way to avoid this pandemic and also made clear that Prevention is better than Cure. The main containment strategy is to reduce the contagion by the isolation of affected individuals. Earlier stage this pandemic was declared as a sort of Pneumonia where an individual gets affected by cold, fever and headache. Later, some new symptoms are seen in affected people like sore throat, breathing problems, and sometimes constipation. To make rapid decisions on treatment, and isolation needs, it would be useful to determine which symptoms presented by suspected infection cases are the best predictors of a positive diagnosis. This can be done by analyzing patient's symptoms and its outcome. Here, we developed a model that employed supervised machine learning algorithms to identify the certain features predicting COVID-19 disease diagnosis with high accuracy. Features examined includes details of the concerned individual, e.g., age, gender, observation of fever, breathing difficulty, and clinical details such as the severity of cough and incidence of lung infection and congestion. We had implemented some Machine Learning techniques with algorithms and found out the highest accuracy more than (50 %) of individual patient for all age groups. The following data is collected from COVID-19 positive patients, online survey and social survey done at testing centres. After that we had applied various methods as Data Preprocessing, Model Validation and Statistical analysis, etc. The probability and accuracy of a patient is shown in using various methods of Machine learning algorithm for a better understanding.
Annals of Translational Medicine, 2021
Background: We developed and validated a machine learning diagnostic model for the novel coronavirus (COVID-19) disease, integrating artificial-intelligence-based computed tomography (CT) imaging and clinical features. Methods: We conducted a retrospective cohort study in 11 Japanese tertiary care facilities that treated COVID-19 patients. Participants were tested using both real-time reverse transcription polymerase chain reaction (RT-PCR) and chest CTs between January 1 and May 30, 2020. We chronologically split the dataset in each hospital into training and test sets, containing patients in a 7:3 ratio. A Light Gradient Boosting Machine model was used for the analysis. Results: A total of 703 patients were included, and two models-the full model and the A-blood modelwere developed for their diagnosis. The A-blood model included eight variables (the Ali-M3 confidence, along with seven clinical features of blood counts and biochemistry markers). The areas under the receiveroperator curve of both models [0.91, 95% confidence interval (CI): 0.86 to 0.95 for the full model and 0.90, 95% CI: 0.86 to 0.94 for the A-blood model] were better than that of the Ali-M3 confidence (0.78, 95% CI: 0.71 to 0.83) in the test set. Conclusions: The A-blood model, a COVID-19 diagnostic model developed in this study, combines machine-learning and CT evaluation with blood test data and performs better than the Ali-M3 framework existing for this purpose. This would significantly aid physicians in making a quicker diagnosis of COVID-19.
Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications
ArXiv, 2020
Purpose: Since the recent COVID-19 outbreak, there has been an avalanche of research papers applying deep learning based image processing to chest radiographs for detection of the disease. To test the performance of the two top models for CXR COVID-19 diagnosis on external datasets to assess model generalizability. Methods: In this paper, we present our argument regarding the efficiency and applicability of existing deep learning models for COVID-19 diagnosis. We provide results from two popular models - COVID-Net and CoroNet evaluated on three publicly available datasets and an additional institutional dataset collected from EMORY Hospital between January and May 2020, containing patients tested for COVID-19 infection using RT-PCR. Results: There is a large false positive rate (FPR) for COVID-Net on both ChexPert (55.3%) and MIMIC-CXR (23.4%) dataset. On the EMORY Dataset, COVID-Net has 61.4% sensitivity, 0.54 F1-score and 0.49 precision value. The FPR of the CoroNet model is signi...
An automated machine learning model for diagnosing coronavirus disease 2019 (COVID-19) infection
IAES International Journal of Artificial Intelligence (IJ-AI)
The coronavirus disease 2019 (COVID-19) epidemic still impacts every facet of life and necessitates a fast and accurate diagnosis. The need for an effective, rapid, and precise way to reduce radiologists' workload in diagnosing suspected cases has emerged. This study used the tree-based pipeline optimization tool (TPOT) and many machine learning (ML) algorithms. TPOT is an open-source genetic programming-based AutoML system that optimizes a set of feature preprocessors and ML models to maximize classification accuracy on a supervised classification problem. A series of trials and comparisons with the results of ML and earlier studies discovered that most of the AutoML beat traditional ML in terms of accuracy. A blood test dataset that has 111 variables and 5644 cases were used. In TPOT, 450 pipelines were used, and the best pipeline selected consisted of radial basis function (RBF) Sampler preprocessing and Gradient boosting classifier as the best algorithm with a 99% accuracy r...
Pakistan Journal of Engineering and Technology
Since breaking out in Wuhan, China in the last days of 2019, the novel COVID-19 pandemic has done a great deal of damage to the mankind; whether it is economic damage, psychological or social one. It was declared a pandemic in March 2020. PCR (Polymerase Chain Reaction) which is used to diagnose COVID-19 patients usually takes 24-72 hours ranging in different countries. A new idea of diagnosing COVID-19 with the help of radiography images has surfaced which has taken research world by storm. There are different machine learning models developed with the help of historic data i.e., datasets which classify COVID-19 patients within a few minutes. As there are different publicly available datasets on which dozens of models are developed, we would like to perform comparative analysis of these datasets. This would help us to identify different aspects of these datasets.
Environmental Science and Pollution Research
As the whole world is witnessing what novel coronavirus (COVID-19) can do to the mankind, it presents several unique features also. In the absence of specific vaccine for COVID-19, it is essential to detect the disease at an early stage and isolate an infected patient. Till today there is a global shortage of testing labs and testing kits for COVID-19. This paper discusses about the role of machine learning techniques for getting important insights like whether lung computed tomography (CT) scan should be the first screening/alternative test for real-time reverse transcriptase-polymerase chain reaction (RT-PCR), is COVID-19 pneumonia different from other viral pneumonia and if yes how to distinguish it using lung CT scan images from the carefully selected data of lung CT scan COVID-19-infected patients from the hospitals of Italy, China, Moscow and India? For training and testing the proposed system, custom vision software of Microsoft azure based on machine learning techniques is used. An overall accuracy of almost 91% is achieved for COVID-19 classification using the proposed methodology.
Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study
Journal of Medical Systems
The COVID-19 pandemia due to the SARS-CoV-2 coronavirus, in its first 4 months since its outbreak, has to date reached more than 200 countries worldwide with more than 2 million confirmed cases (probably a much higher number of infected), and almost 200,000 deaths. Amplification of viral RNA by (real time) reverse transcription polymerase chain reaction (rRT-PCR) is the current gold standard test for confirmation of infection, although it presents known shortcomings: long turnaround times (3-4 hours to generate results), potential shortage of reagents, false-negative rates as large as 15-20%, the need for certified laboratories, expensive equipment and trained personnel. Thus there is a need for alternative, faster, less expensive and more accessible tests. We developed two machine learning classification models using hematochemical values from routine blood exams (namely: white blood cells counts, and the platelets, CRP, AST, ALT, GGT, ALP, LDH plasma levels) drawn from 279 patients who, after being admitted to the San Raffaele Hospital (Milan, Italy) emergencyroom with COVID-19 symptoms, were screened with the rRT-PCR test performed on respiratory tract specimens. Of these patients, 177 resulted positive, whereas 102 received a negative response. We have developed two machine learning models, to discriminate between patients who are either positive or negative to the SARS-CoV-2: their accuracy ranges between 82% and 86%, and sensitivity between 92% e 95%, so comparably well with respect to the gold standard. We also developed an interpretable Decision Tree model as a simple decision aid for clinician interpreting blood tests (even off-line) for COVID-19 suspect cases. This study demonstrated the feasibility and clinical soundness of using blood tests analysis and machine learning as an alternative to rRT-PCR for identifying COVID-19 positive patients. This is especially useful in those countries, like developing ones, suffering from shortages of rRT-PCR reagents and specialized laboratories. We made available a Web-based tool for clinical reference and evaluation (This tool is available at https://covid19-blood-ml.herokuapp.com/).