ABSTRACT
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.
ABSTRACT
The disease caused by the new type of coronavirus, Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-CoV-2 has already caused over 2 million deaths to date. In this work, we propose a web solution, called Heg.IA, to optimize the diagnosis of Covid-19 through the use of artificial intelligence. Our system aims to support decision-making regarding to diagnosis of Covid-19 and to the indication of hospitalization on regular ward, semi-ICU or ICU based on decision a Random Forest architecture with 90 trees. The main idea is that healthcare professionals can insert 41 hematological parameters from common blood tests and arterial gasometry into the system. Then, Heg.IA will provide a diagnostic report. The system reached good results for both Covid-19 diagnosis and to recommend hospitalization. For the first scenario we found average results of accuracy of 92.891%±0.851, kappa index of 0.858 ± 0.017, sensitivity of 0.936 ± 0.011, precision of 0.923 ± 0.011, specificity of 0.921 ± 0.012 and area under ROC of 0.984 ± 0.003. As for the indication of hospitalization, we achieved excellent performance of accuracies above 99% and more than 0.99 for the other metrics in all situations. By using a computationally simple method, based on the classical decision trees, we were able to achieve high diagnosis performance. Heg.IA system may be a way to overcome the testing unavailability in the context of Covid-19.Communicated by Ramaswamy H. Sarma.
ABSTRACT
In this retrospective cohort study, we investigated the formation of individual classes of antibodies to SARS-CoV-2 in archived serial sera from hospitalized patients with the medium-severe (n = 17) and severe COVID-19 (n = 11). The serum/plasma samples were studied for the presence of IgG, IgM and IgA antibodies to the recombinant S- and N-proteins of SARS-CoV-2. By the 7th day of hospitalization, an IgG increase was observed in patients both with a positive PCR test and without PCR confirmation of SARS-CoV-2 infection. Significant increases in the anti-spike IgG levels were noted only in moderate COVID-19. The four-fold increase of IgM to N-protein was obtained more often in the groups with mild and moderate infections. The IgA levels decreased during the infection to both the S- and N-proteins, and the most pronounced decrease was in the severe COVID-19 patients. The serum IgG to S-protein one week after hospitalization demonstrated a high-power relationship (rs = 0.75) with the level of RBD antibodies. There was a medium strength relationship between the levels of CRP and IgG (rs = 0.43). Thus, in patients with acute COVID-19, an increase in antibodies can develop as early as 1 week of hospital stay. The SARS-CoV-2 antibody conversions may confirm SARS-CoV-2 infection in PCR-negative patients.
ABSTRACT
Background: In the era of coronavirus disease 2019 (COVID-19), it is mandatory to identify vulnerable people with cancers as they have impaired immune system that can lead to high mortality. This study analyzes the complete blood count (CBC) derived inflammatory biomarkers and the level of anti-SARS-CoV-2 neutralizing antibody (NAb) and spike protein's receptor-binding domain immunoglobulin G (S-RBD IgG) among cancer survivors. Methods: A cross-sectional study was conducted in patients with either solid or hematological cancers who had received two-doses of COVID-19 vaccinations within six months. Results: From 119 subjects, the COVID-19 vaccines demonstrated laboratory efficacy (median NAb=129.03 AU/mL;median S-RBD IgG=270.53 AU/mL). The seropositive conversion of NAb reached 94.1% and S-RBD IgG reached 93.3%. Additionally, the S-RBD IgG had very weak correlation with absolute monocyte count (R=-0.185;p-value=0.044). The NAb also had very weak correlation with leukocyte (Kendall's tau-b (τb)=-0.147;p-value=0.019), absolute neutrophil count (τb=-0.126;p-value=0.044), absolute eosinophil count (τb=-0.132;p-value=0.034). Conclusion: The seropositivity rate of anti-SARS-CoV-2 NAb and S-RBD IgG were significantly high. However, the CBC derived inflammatory biomarkers had poor correlation with anti-SARS-CoV-2 NAb and S-RBD IgG. Thus, anti-SARS-CoV-2 NAb and S-RBD IgG are currently the only reliable markers for measuring the COVID-19 vaccine efficacy which should be widely accessible.
ABSTRACT
SARS-CoV2 (COVID-19) is the virus that causes the pandemic that has severely impacted human society with a massive death toll worldwide. Hence, there is a persistent need for fast and reliable automatic tools to help health teams in making clinical decisions. Predictive models could potentially ease the strain on healthcare systems by early and reliable screening of COVID-19 patients which helps to combat the spread of the disease. Recent studies have reported some key advantages of employing routine blood tests for initial screening of COVID-19 patients. Thus, in this paper, we propose a novel COVID-19 prediction model based on routine blood tests. In this model, we depend on exploiting the real dependency among the employed feature pool by a sparsification procedure. In this sparse domain, a hybrid feature selection mechanism is proposed. This mechanism fuses the selected features from two perspectives, the first is Pearson correlation and the second is a new Minkowski-based equilibrium optimizer (MEO). Then, the selected features are fed into a new 1D Convolutional Neural Network (1DCNN) for a final diagnosis decision. The proposed prediction model is tested with a new public dataset from San Raphael Hospital, Milan, Italy, i.e., OSR dataset which has two sub-datasets. According to the experimental results, the proposed model outperforms the state-of-the-art techniques with an average testing accuracy of 98.5% while we employ only less than half the size of the feature pool, i.e., we need only less than half the given blood tests in the employed dataset to get a final diagnosis decision.
ABSTRACT
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.
ABSTRACT
Background: Coronavirus disease 2019 (COVID-19) has resulted in millions of mortality cases and significant incremental costs to the healthcare system. Examination of CRP and D-dimer were considered to have higher costs, and the use of simple hematological parameters such as lymphocyte, neutrophil, and white blood cell (WBC) which have more affordable costs would be cost-saving. Radiological imaging complements clinical evaluation and laboratory parameters for managing COVID-19 patients. Therefore, categorizing patients into severe or non-severe becomes more defined, allowing for earlier interventions and decisions of hospital admission or being referred to a tertiary hospital. Purpose: To evaluate the variables correlated with poor outcomes in COVID-19 patients. Patients and Methods: This was a retrospective study on COVID-19 patients in a secondary referral hospital in treating COVID-19 in Indonesia. Demographic, clinical data, laboratory parameters, CXR (analyzed using a modified scoring system), and prognosis were collected through electronic nursing and medical records. Results: This study included 476 hospitalized COVID-19 patients. Severe patients were commonly found with older age (median of 57 vs 40), dyspnea (percentage of 85.2% vs 20.5%), higher CXR score (median of 7 vs 5), higher levels of neutrophil (median of 79.9 vs 68.3), and lower lymphocyte levels (median of 13.4 vs 22.7), compared to non-severe patients. These variables were known to increase the odds of severe disease. Older age (median of 57 vs 48), SpO2 <94% room air (percentage of 87.4% vs 31.5%), higher CXR score (median of 8 vs 5), and higher respiratory rate (median of 25 vs 20) were found higher in death patients and were known to increase the odds of death outcome. Conclusion: The simple blood tests (neutrophil and lymphocyte) and modified CXR scoring system are useful in risk stratification for severe disease and mortality in COVID-19 patients to decide the earlier interventions and treatment.