Development, evaluation and validation of machine learning algorithms to detect atypical and asymptomatic presentations of Covid-19 in hospital practice

Discrimination between Covid-19 positive and negative blood serum based on excitation-emission matrix fluorescence spectroscopy and chemometrics

The outbreak of the disease caused by the SARS-CoV-2 virus (Covid-19) has resulted in a global health emergency that has caused millions of deaths in recent years. The control of the pandemic was significantly impacted by the availability of inputs and qualified labor to correctly diagnose the population. The challenges faced by numerous countries in conducting this extensive diagnosis, utilizing methods such as RT-PCR, emphasize the necessity for alternative testing strategies that are less reliant on expensive raw materials and can be implemented on a larger scale. This paper proposes a methodology for classifying blood serum samples as either positive or negative for Covid-19 infection using excitation-emission matrix (EEM) fluorescence spectroscopy associated with multivariate analysis. The proposed methodology uses EEM spectra of samples diagnosed by the reference method (RT-PCR) to train and validate classification models. Two approaches were tested: the first using PARAFAC and the second by unfolding the excitation-emission matrices. The DD-SIMCA model performed best in the PARAFAC approach, with an error rate of 0.05, sensitivity of 0.98 and specificity of 0.96. The PLS-DA and PCA-DA models in the second approach effectively distinguished between classes. The PCA-DA model performed the best with an error rate of 0.06 and sensitivity and specificity of 0.94. Fluorescence spectroscopy was found to be effective in analyzing serum samples and obtaining discrimination models to determine if a patient is infected with SARS-CoV-2. The findings are encouraging and could aid in the development of an inexpensive and reliable auxiliary diagnostic method.

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images

BACKGROUND

Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) to develop a prediction model based on CT characteristics for the identification of asymptomatic carriers.

METHODS

Asymptomatic carriers were from Yangzhou Third People's Hospital from August 1st, 2020, to March 31st, 2021, and the control group included a healthy population from a nonepizootic area with two negative RT‒PCR results within 48 h. All CT images were preprocessed using MATLAB. Model development and validation were conducted in R with the H2O package. The models were built based on six algorithms, e.g., random forest and deep neural network (DNN), and a training set (n = 691). The models were improved by automatically adjusting hyperparameters for an internal validation set (n = 306). The performance of the obtained models was evaluated based on a dataset from Suzhou (n = 178) using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score.

RESULTS

A total of 1,175 images were preprocessed with high stability. Six models were developed, and the performance of the DNN model ranked first, with an AUC value of 0.898 for the test set. The sensitivity, specificity, PPV, NPV, F1 score and accuracy of the DNN model were 0.820, 0.854, 0.849, 0.826, 0.834 and 0.837, respectively. A plot of a local interpretable model-agnostic explanation demonstrated how different variables worked in identifying asymptomatic carriers.

CONCLUSIONS

Our study demonstrates that AutoML models based on CT images can be used to identify asymptomatic carriers. The most promising model for clinical implementation is the DNN-algorithm-based model.

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

The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using the most practical medicine facilities. But blood tests give general information about a patient's state, which is not directly associated with COVID-19. COVID-19-specific features should be selected from the list of standard blood characteristics, and decision-making software based on appropriate clinical data should be created. This review describes the abilities to develop predictive models for COVID-19 detection using routine blood tests and machine learning.