LogNNet model as a fast, simple and economical AI instrument in the diagnosis and prognosis of COVID-19.

Rapid and effective detection of the diagnosis and prognosis of COVID-19 disease is important in terms of reducing the mortality of the disease and reducing the pressure on health systems. Methods such as PCR testing and computed tomography used for this purpose in current health systems are costly, require an expert team and take time. This study offers a fast, economical and reliable approach for the early diagnosis and prognosis of infectious diseases, especially COVID-19. For this purpose, characteristics of a large population of COVID-19 patients were determined (51 different routine blood values) and calibrated. In order to determine the diagnosis and prognosis of the disease, the calibrated features were run with the LogNNet model. LogNNet has a simple algorithm and performance indicators comparable to the most efficient algorithms available.This approach pointed out that routine blood values contain important information, especially in the detection of COVID-19, and showed that the LogNNet model can be used as an economical, safe and fast alternative tool in the diagnosis of this disease.-In the LogNNet feedforward neural network, a feature vector is passed through a specially designed reservoir matrix and transformed into a new feature vector of a different size, increasing the classification accuracy.-The presented network architecture can achieve 80%-99% classification accuracy using a range of weightings on devices with a total memory size of 1 to 29 kB constrained.-Due to the chaotic mapping procedures, the RAM usage in the LogNNet neural network processing process is greatly reduced. Hence, optimization of chaotic map parameters has an important function in LogNNet neural network application.

Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models.

Objectives: When the prognosis of COVID-19 disease can be detected early, the intense-pressure and loss of workforce in health-services can be partially reduced. The primary-purpose of this article is to determine the feature-dataset consisting of the routine-blood-values (RBV) and demographic-data that affect the prognosis of COVID-19. Second, by applying the feature-dataset to the supervised machine-learning (ML) models, it is to identify severely and mildly infected COVID-19 patients at the time of admission. Material and methods: The sample of this study consists of severely (n = 192) and mildly (n = 4010) infected-patients hospitalized with the diagnosis of COVID-19 between March-September, 2021. The RBV-data measured at the time of admission and age-gender characteristics of these patients were analyzed retrospectively. For the selection of the features, the minimum-redundancy-maximum-relevance (MRMR) method, principal-components-analysis and forward-multiple-logistics-regression analyzes were used. The features set were statistically compared between mild and severe infected-patients. Then, the performances of various supervised-ML-models were compared in identifying severely and mildly infected-patients using the feature set. Results: In this study, 28 RBV-parameters and age-variable were found as the feature-dataset. The effect of features on the prognosis of the disease has been clinically proven. The ML-models with the highest overall-accuracy in identifying patient-groups were found respectively, as follows: local-weighted-learning (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor (KNN)-94.05%. Also, the most successful models with the highest area-under-the-receiver-operating-characteristic-curve (AUC) values in identifying patient groups were found respectively, as follows: LWL-0.95%, K*-0.91%, NB-0.85% and KNN-0.75%. Conclusion: The findings in this article have significant a motivation for the healthcare professionals to detect at admission severely and mildly infected COVID-19 patients.

A Spectrum of Bacterial Sepsis in Covid-19 Positive and Non Covid-19 Patients - an Indian Monocenter Retrospective Study

-This study aims to determine spectrum of bacterial infection in patients with severe acute respiratory syndrome coronavirus-2 infection at the time of hospital admission and identify changes in hematological biomarkers of COVID19 severity. A retrospective study was conducted in blood cultures from patients suspected to have sepsis were included in the study. Patients were grouped based on SARS-CoV-2 RT-PCR result as positive, negative, or not tested. For the purposes of classifying blood cultures by SARS-CoV-2 RT-PCR status, hematological parameters were analyzed. Out off 825 blood sample, 466 samples was positive for blood culture identified by conventional and Automatic blood culture system - Vitek 2. Among these 466 patients, 211(45.2%) were positive for SARS-CoV2 virus and 255 (54.7%) were negative for SARS-CoV2 virus by RT-PCR. Regarding CRP Total number of CRP positive samples was 654, CRP negative samples was 84 and CRP was not done for 87samples. The total number of IL6 positive samples was 83 and IL6 negative samples were not evaluated for 125 patients. The concern of Bacterial sepsis in COVID-19 patients due to the above organisms based on our study over the period of one year. Consequently, it is important to pay attention to bacterial co-infections in critical patients diagnosed for COVID-19. Copyright © Global Science Publications.

Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application.

Healthcare digitalization requires effective applications of human sensors, when various parameters of the human body are instantly monitored in everyday life due to the Internet of Things (IoT). In particular, machine learning (ML) sensors for the prompt diagnosis of COVID-19 are an important option for IoT application in healthcare and ambient assisted living (AAL). Determining a COVID-19 infected status with various diagnostic tests and imaging results is costly and time-consuming. This study provides a fast, reliable and cost-effective alternative tool for the diagnosis of COVID-19 based on the routine blood values (RBVs) measured at admission. The dataset of the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 test results and 51 routine blood values. In this study, 13 popular classifier machine learning models and the LogNNet neural network model were exanimated. The most successful classifier model in terms of time and accuracy in the detection of the disease was the histogram-based gradient boosting (HGB) (accuracy: 100%, time: 6.39 sec). The HGB classifier identified the 11 most important features (LDL, cholesterol, HDL-C, MCHC, triglyceride, amylase, UA, LDH, CK-MB, ALP and MCH) to detect the disease with 100% accuracy. In addition, the importance of single, double and triple combinations of these features in the diagnosis of the disease was discussed. We propose to use these 11 features and their binary combinations as important biomarkers for ML sensors in the diagnosis of the disease, supporting edge computing on Arduino and cloud IoT service.

Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network.

Since February 2020, the world has been engaged in an intense struggle with the COVID-19 disease, and health systems have come under tragic pressure as the disease turned into a pandemic. The aim of this study is to obtain the most effective routine blood values (RBV) in the diagnosis and prognosis of COVID-19 using a backward feature elimination algorithm for the LogNNet reservoir neural network. The first dataset in the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 tests. The LogNNet-model achieved the accuracy rate of 99.5% in the diagnosis of the disease with 46 features and the accuracy of 99.17% with only mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, and activated partial prothrombin time. The second dataset consists of a total of 3899 patients with a diagnosis of COVID-19 who were treated in hospital, of which 203 were severe patients and 3696 were mild patients. The model reached the accuracy rate of 94.4% in determining the prognosis of the disease with 48 features and the accuracy of 82.7% with only erythrocyte sedimentation rate, neutrophil count, and C reactive protein features. Our method will reduce the negative pressures on the health sector and help doctors to understand the pathogenesis of COVID-19 using the key features. The method is promising to create mobile health monitoring systems in the Internet of Things.

High immature platelet fraction with reduced platelet count on hospital admission. Can it be useful for COVID-19 diagnosis?

INTRODUCTION: Health professions are heavily engaged facing the current threat of SARS-CoV-2 (COVID-19). Although there are many diagnostic tools, an accurate and rapid laboratory procedure for diagnosing COVID-19 is recommended. We focused on platelet parameters as the additional biomarkers for clinical diagnosis in patients presenting to the emergency department (ED). MATERIALS AND METHODS: Five hundred and sixty-one patients from February to April 2020 have been recruited. Patients were divided into three groups: (N = 50) COVID-19 positive and (N = 21) COVID-19 negative with molecular testing, (N = 490) as reference population without molecular testing. A Multiplex rRT-PCR from samples collected by nasopharyngeal swabs was performed and the hematological data collected. RESULTS: We detected a mild anemia in COVID-19 group and lymphopenia against reference population: hemoglobin (g/dL) 13.0 (11.5-14.8) versus 13.9 (12.8-15.0) (P = .0135); lymphocytes (109 /L) 1.24 (0.94-1.73) versus 1.99 (1.49-2.64) (P < .0001). In addition, abnormal platelet parameters as follows (COVID group vs reference population): PLT (×109 /L) 209 (160-258) vs 236 (193-279) (P = .0239). IPF (%) 4.05 (2.5-5.9) versus 3.4 (2.2-4.9) (P = .0576); H-IPF (%) 1.25 (0.8-2.2) versus 0.95 (0.6-1.5) (P = .0171) were identified. In particular, COVID positive group had a high H-IPF/IPF Ratio compared to reference population [0.32 (0.29-0.36) versus 0.29 (0.26-0.32), respectively, (P = .0003)]. Finally, a PLT difference of nearly 50 × 109 /L between pre/postCOVID-19 sampling for each patient was found (N = 42) (P = .0194). CONCLUSIONS: COVID-19 group results highlighted higher IPF and H-IPF values, with increased H-IPF/IPF Ratio, associated to PLT count reduction. These findings shall be adopted for a timely diagnosis of patients upon hospital admission.

Biomarkers associated with COVID-19 disease progression.

The coronavirus disease 2019 (COVID-19) pandemic is a scientific, medical, and social challenge. The complexity of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is centered on the unpredictable clinical course of the disease that can rapidly develop, causing severe and deadly complications. The identification of effective laboratory biomarkers able to classify patients based on their risk is imperative in being able to guarantee prompt treatment. The analysis of recently published studies highlights the role of systemic vasculitis and cytokine mediated coagulation disorders as the principal actors of multi organ failure in patients with severe COVID-19 complications. The following biomarkers have been identified: hematological (lymphocyte count, neutrophil count, neutrophil-lymphocyte ratio (NLR)), inflammatory (C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), procalcitonin (PCT)), immunological (interleukin (IL)-6 and biochemical (D-dimer, troponin, creatine kinase (CK), aspartate aminotransferase (AST)), especially those related to coagulation cascades in disseminated intravascular coagulation (DIC) and acute respiratory distress syndrome (ARDS). New laboratory biomarkers could be identified through the accurate analysis of multicentric case series; in particular, homocysteine and angiotensin II could play a significant role.