Predictive models for COVID-19 detection using routine blood tests and machine learning

Early biomarkers in hospitalized patients as predictors of post-acute sequelae of SARS-CoV-2 infection: a one-year cohort study

BACKGROUND

Post-acute sequelae of SARS-CoV-2 infection (PASC) represent a significant challenge in patient care, with symptoms persisting beyond three month's post-recovery. This study aimed to evaluate the incidence of PASC at one year post-COVID-19 and identify predictive biomarkers and comorbidities for effective risk stratification.

METHODS

A cohort of 120 adult patients, including 50 intensive care and 70 non-intensive care patients, was followed up at two weeks, six weeks, and one-year post-discharge using structured questionnaires. The study integrated comorbidities and laboratory biomarkers to forecast the risk for PASC.

RESULTS

The median age of participants was 56 years, with 40% having moderate to severe comorbidities. A year post-recovery, 32.8% exhibited post COVID-19 conditions. The most common symptoms were constitutional (16%), respiratory (8.4%), and neuropsychiatric (2.5%). Bayesian network analysis indicated significant correlations between constitutional symptoms, rehospitalisation, and biomarkers including C-reactive protein, lactate-dehydrogenase, ferritin, and albumin.

CONCLUSION

This study highlights the prolonged impact of PASC, one-year post infection. It highlights the role of specific biomarkers such as C-reactive protein, lactate-dehydrogenase, ferritin, and albumin in tailoring individual patient care by advancing understanding in post-COVID-19 symptoms prediction. Our findings support the need for further research to refine these insights, which are pivotal for the ongoing care of patients in the aftermath of COVID-19.

Determination of prognostic markers for COVID-19 disease severity using routine blood tests and machine learning

The need for the identification of risk factors associated to COVID-19 disease severity remains urgent. Patients' care and resource allocation can be potentially different and are defined based on the current classification of disease severity. This classification is based on the analysis of clinical parameters and routine blood tests, which are not standardized across the globe. Some laboratory test alterations have been associated to COVID-19 severity, although these data are conflicting partly due to the different methodologies used across different studies. This study aimed to construct and validate a disease severity prediction model using machine learning (ML). Seventy-two patients admitted to a Brazilian hospital and diagnosed with COVID-19 through RT-PCR and/or ELISA, and with varying degrees of disease severity, were included in the study. Their electronic medical records and the results from daily blood tests were used to develop a ML model to predict disease severity. Using the above data set, a combination of five laboratorial biomarkers was identified as accurate predictors of COVID-19 severe disease with a ROC-AUC of 0.80 ​±​ 0.13. Those biomarkers included prothrombin activity, ferritin, serum iron, ATTP and monocytes. The application of the devised ML model may help rationalize clinical decision and care.

Stacked deep learning approach for efficient SARS-CoV-2 detection in blood samples

Identifying COVID-19 through blood sample analysis is crucial in managing the disease and improving patient outcomes. Despite its advantages, the current test demands certified laboratories, expensive equipment, trained personnel, and 3-4 h for results, with a notable false-negative rate of 15%-20%. This study proposes a stacked deep-learning approach for detecting COVID-19 in blood samples to distinguish uninfected individuals from those infected with the virus. Three stacked deep learning architectures, namely the StackMean, StackMax, and StackRF algorithms, are introduced to improve the detection quality of single deep learning models. To counter the class imbalance phenomenon in the training data, the Synthetic Minority Oversampling Technique (SMOTE) algorithm is also implemented, resulting in increased specificity and sensitivity. The efficacy of the methods is assessed by utilizing blood samples obtained from hospitals in Brazil and Italy. Results revealed that the StackMax method greatly boosted the deep learning and traditional machine learning methods' capability to distinguish COVID-19-positive cases from normal cases, while SMOTE increased the specificity and sensitivity of the stacked models. Hypothesis testing is performed to determine if there is a significant statistical difference in the performance between the compared detection methods. Additionally, the significance of blood sample features in identifying COVID-19 is analyzed using the XGBoost (eXtreme Gradient Boosting) technique for feature importance identification. Overall, this methodology could potentially enhance the timely and precise identification of COVID-19 in blood samples.

Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases

Healthcare is a topic of significant concern within the academic and business sectors. The COVID-19 pandemic has had a considerable effect on the health of people worldwide. The rapid increase in cases adversely affects a nation's economy, public health, and residents' social and personal well-being. Improving the precision of COVID-19 infection forecasts can aid in making informed decisions regarding interventions, given the pandemic's harmful impact on numerous aspects of human life, such as health and the economy. This study aims to predict the number of confirmed COVID-19 cases in Saudi Arabia using Bayesian optimization (BOA) and deep learning (DL) methods. Two methods were assessed for their efficacy in predicting the occurrence of positive cases of COVID-19. The research employed data from confirmed COVID-19 cases in Saudi Arabia (SA), the United Kingdom (UK), and Tunisia (TU) from 2020 to 2021. The findings from the BOA model indicate that accurately predicting the number of COVID-19 positive cases is difficult due to the BOA projections needing to align with the assumptions. Thus, a DL approach was utilized to enhance the precision of COVID-19 positive case prediction in South Africa. The DQN model performed better than the BOA model when assessing RMSE and MAPE values. The model operates on a local server infrastructure, where the trained policy is transmitted solely to DQN. DQN formulated a reward function to amplify the efficiency of the DQN algorithm. By examining the rate of change and duration of sleep in the test data, this function can enhance the DQN model's training. Based on simulation findings, it can decrease the DQN work cycle by roughly 28% and diminish data overhead by more than 50% on average.

Association of triglyceride/high-density lipoprotein cholesterol ratio with severe complications of COVID-19

Background

The coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus can lead to serious complications such as respiratory failure, requiring mechanical ventilation or ICU care, and can even result in death, especially in older patients with comorbidities. The ratio of triglyceride to high-density lipoprotein cholesterol (TG/HDL), a biomarker of atherosclerotic dyslipidemia and insulin resistance, is related to cardiovascular mortality and morbidity. We aimed to evaluate the link between serious complications of COVID-19 and TG/HDL in the general population.

Methods

We conducted a comprehensive analysis of 3,933 COVID-19 patients from a nationwide cohort in Korea spanning from January 1 to June 4, 2020. TG/HDL ratio was calculated from the national health screening examination data underwent before the COVID-19 infection. Serious complications of COVID-19 were defined as a composite of high-flow oxygen therapy, mechanical ventilation, admission to the intensive care unit (ICU), and mortality. We employed logistic regression analysis to investigate the relationship between the TG/HDL ratio and the likelihood of developing severe complications within 2 months of the diagnosis. To visualize this association, we used a smoothing spline plot based on the generalized additive regression model. Multivariate analysis was performed with adjustment for age, gender, body mass index, lifestyle measures, and comorbidities.

Results

Among the 3,933 COVID-19 patients, the proportion of serious complications was 7.53%. Regarding individual outcomes, the number of patients who received high-flow oxygen therapy, mechanical ventilation, ICU care, and died was 84 (2.14%), 122 (3.10%), 173 (4.40%), and 118 (3.00%), respectively. In the multivariable logistic regression, a positive association was found between TG/HDL ratio and serious complications of COVID-19 (adjusted OR, 1.09; 95% CI [1.03-1.15], p = 0.004).

Conclusion

Our study revealed a significant positive association between TG/HDL ratio and the risk of developing severe complications in COVID-19-infected patients. While this finding provides valuable insight into the potential prognostic role of TG/HDL ratio in COVID-19, further studies are needed to fully elucidate the underlying mechanisms behind this relationship.

Diagnosis of COVID-19 from blood parameters using convolutional neural network

Asymptomatically presenting COVID-19 complicates the detection of infected individuals. Additionally, the virus changes too many genomic variants, which increases the virus's ability to spread. Because there isn't a specific treatment for COVID-19 in a short time, the essential goal is to reduce the virulence of the disease. Blood parameters, which contain essential clinical information about infectious diseases and are easy to access, have an important place in COVID-19 detection. The convolutional neural network (CNN) architecture, which is popular in image processing, produces highly successful results for COVID-19 detection models. When the literature is examined, it is seen that COVID-19 studies with CNN are generally done using lung images. In this study, one-dimensional (1D) blood parameters data were converted into two-dimensional (2D) image data after preprocessing, and COVID-19 detection was made with CNN. The t-distributed stochastic neighbor embedding method was applied to transfer the feature vectors to the 2D plane. All data were framed with convex hull and minimum bounding rectangle algorithms to obtain image data. The image data obtained by pixel mapping was presented to the developed 3-line CNN architecture. This study proposes an effective and successful model by providing a combination of low-cost and rapidly-accessible blood parameters and CNN architecture making image data processing highly successful for COVID-19 detection. Ultimately, COVID-19 detection was made with a success rate of 94.85%. This study has brought a new perspective to COVID-19 detection studies by obtaining 2D image data from 1D COVID-19 blood parameters and using CNN.

Deep Learning Based COVID-19 Detection via Hard Voting Ensemble Method

Healthcare systems throughout the world are under a great deal of strain because to the continuing COVID-19 epidemic, making early and precise diagnosis critical for limiting the virus's propagation and efficiently treating victims. The utilization of medical imaging methods like X-rays can help to speed up the diagnosis procedure. Which can offer valuable insights into the virus's existence in the lungs. We present a unique ensemble approach to identify COVID-19 using X-ray pictures (X-ray-PIC) in this paper. The suggested approach, based on hard voting, combines the confidence scores of three classic deep learning models: CNN, VGG16, and DenseNet. We also apply transfer learning to enhance performance on small medical image datasets. Experiments indicate that the suggested strategy outperforms current techniques with a 97% accuracy, a 96% precision, a 100% recall, and a 98% F1-score.These results demonstrate the effectiveness of using ensemble approaches and COVID-19 transfer-learning diagnosis using X-ray-PIC, which could greatly aid in early detection and reducing the burden on global health systems.

Semi-Supervised KPCA-Based Monitoring Techniques for Detecting COVID-19 Infection through Blood Tests

This study introduces a new method for identifying COVID-19 infections using blood test data as part of an anomaly detection problem by combining the kernel principal component analysis (KPCA) and one-class support vector machine (OCSVM). This approach aims to differentiate healthy individuals from those infected with COVID-19 using blood test samples. The KPCA model is used to identify nonlinear patterns in the data, and the OCSVM is used to detect abnormal features. This approach is semi-supervised as it uses unlabeled data during training and only requires data from healthy cases. The method's performance was tested using two sets of blood test samples from hospitals in Brazil and Italy. Compared to other semi-supervised models, such as KPCA-based isolation forest (iForest), local outlier factor (LOF), elliptical envelope (EE) schemes, independent component analysis (ICA), and PCA-based OCSVM, the proposed KPCA-OSVM approach achieved enhanced discrimination performance for detecting potential COVID-19 infections. For the two COVID-19 blood test datasets that were considered, the proposed approach attained an AUC (area under the receiver operating characteristic curve) of 0.99, indicating a high accuracy level in distinguishing between positive and negative samples based on the test results. The study suggests that this approach is a promising solution for detecting COVID-19 infections without labeled data.