Explainable AI for COVID-19 prognosis from early Chest X-ray and clinical data in the context of the COVID-CXR international hackathon

Although many AI-based scientific works regarding chest X-ray (CXR) interpretation focused on COVID-19 diagnosis, fewer papers focused on other relevant tasks, like severity estimation, deterioration, and prognosis. The same holds for explainable decisions to estimate COVID-19 prognosis as well. The international hackathon launched during Dubai Expo 2020, aimed at designing machine learning solutions to help physicians formulate COVID-19 patients' prognosis, was the occasion to develop a machine learning model capable of predicting such prognoses and justifying them through interpretable explanations. The large hackathon dataset comprised subjects characterized by their CXR and numerous clinical features collected during triage. To calculate the prognostic value, our model considered both patients' CXRs and clinical features. After automatic pre-processing to improve their quality, CXRs were processed by a Deep Learning model to estimate the lung compromise degree, which has been considered as an additional clinical feature. Original clinical parameters suffered from missing values that were adequately handled. We trained and evaluated multiple models to find the best one and fine-tune it before the inference process. Finally, we produced novel explanations, both visual and numerical, to justify the model predictions. Ultimately, our model processes a CXR and several clinical data to estimate a patient's prognosis related to the COVID-19 disease. It proved to be accurate and was ranked second in the final rankings with 75%, 73.9%, and 74.4% in sensitivity, specificity, and balanced accuracy, respectively. In terms of model explainability, it was ranked first since it was agreed to be the most interpretable by health professionals. © 2023 SPIE.

A Hand Gesture-Based Contact-less Interface for Electronic Health Records

Clinical data monitoring and storing are essential components of continuous and preventive healthcare systems. Data such as blood pressure, pulse rate, temperature, etc., can be recorded by the hospital staff daily for in-patient subjects. The usual way of noting them down is to check different parameters using various medical instruments and write it on paper with the corresponding patient's details (e.g., name, patient-id, or government identity card number). However, after the outbreak of COVID-19, there is a set of World Health Organization (WHO) guidelines to behave in public places. Ordinary people and professionals feel hesitant to touch any media even if they have some protection such as gloves and sanitizer. In this crisis, there is a natural demand for contact-less activities instead of touch-based traditional ways. Gesture-based activities might be one of the low-cost alternatives to some sensor-based systems. This paper uses a profound learning-based finger point gesture to capture writing in the air and realize it on the screen through a predictive model. Here, the proposed framework has been demonstrated as a proof of concept to record blood pressure data for multiple patients without touching any electronic screen or paper. The proposed architecture is developed based on the gesture recognition and metric learning, which have been used to recognize different digits trained from the MNIST digit dataset. The mean test accuracy is reached 99.47% on the same dataset. © 2022 IEEE.

BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data.

Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March-June 2020). The dataset consists of twenty-five biomarkers from electronic health record and Chest X-ray (CXR) images. It is found that the system can diagnose low- or high-risk patients with an accuracy, sensitivity, and F1-score of 89.03%, 90.44%, and 89.03%, respectively. The system exhibits 6% higher accuracy than the systems that employ either CXR images or biomarker data. In addition, the system can calculate the mortality risk of high-risk patients using multivariate logistic regression-based nomogram scoring technique. Interested physicians can use the presented system to predict the early mortality risks of COVID-19 patients using the web-link: Covid-severity-grading-AI. In this case, a physician needs to input the following information: CXR image file, Lactate Dehydrogenase (LDH), Oxygen Saturation (O2%), White Blood Cells Count, C-reactive protein, and Age. This way, this study contributes to the management of COVID-19 patients by predicting early mortality risk. Supplementary Information: The online version contains supplementary material available at 10.1007/s00521-023-08606-w.

Evaluation of stacked ensemble model performance to predict clinical outcomes: A COVID-19 study.

BACKGROUND: The application of machine learning (ML) to analyze clinical data with the goal to predict patient outcomes has garnered increasing attention. Ensemble learning has been used in conjunction with ML to improve predictive performance. Although stacked generalization (stacking), a type of heterogeneous ensemble of ML models, has emerged in clinical data analysis, it remains unclear how to define the best model combinations for strong predictive performance. This study develops a methodology to evaluate the performance of "base" learner models and their optimized combination using "meta" learner models in stacked ensembles to accurately assess performance in the context of clinical outcomes. METHODS: De-identified COVID-19 data was obtained from the University of Louisville Hospital, where a retrospective chart review was performed from March 2020 to November 2021. Three differently-sized subsets using features from the overall dataset were chosen to train and evaluate ensemble classification performance. The number of base learners chosen from several algorithm families coupled with a complementary meta learner was varied from a minimum of 2 to a maximum of 8. Predictive performance of these combinations was evaluated in terms of mortality and severe cardiac event outcomes using area-under-the-receiver-operating-characteristic (AUROC), F1, balanced accuracy, and kappa. RESULTS: The results highlight the potential to accurately predict clinical outcomes, such as severe cardiac events with COVID-19, from routinely acquired in-hospital patient data. Meta learners Generalized Linear Model (GLM), Multi-Layer Perceptron (MLP), and Partial Least Squares (PLS) had the highest AUROC for both outcomes, while K-Nearest Neighbors (KNN) had the lowest. Performance trended lower in the training set as the number of features increased, and exhibited less variance in both training and validation across all feature subsets as the number of base learners increased. CONCLUSION: This study offers a methodology to robustly evaluate ensemble ML performance when analyzing clinical data.

A New Machine Learning Framework for Detecting COVID-19 From Clinical Data on Lung And Heart Function

Currently, the need for real-time COVID-19 detection methods with minimal tools and cost is an important challenge. The available methods are still difficult to apply, slow, costly, and their accuracy is low. In this work, a novel machine learning-based framework to predict COVID-19 is proposed, which is based on rapid inpatient clinical tests of lung and heart function. Compared with current cognition therapy techniques, the proposed framework can significantly improve the accuracy and time performance of COVID-19 diagnosis without any lab or equipment requirements. In this work, five parameters of clinical testing were adopted;Respiration rate, Heart rate, systolic blood pressure, diastolic blood pressure, and mean arterial blood pressure. After obtaining results for these tests, a pre-trained intelligent model based on Random Forest Tree (RFT) machine learning algorithm is used for detection. This model was trained by about 13,558 records of the COVID19 testing dataset collected from King Faisal Specialist Hospital and Research Centre (KFSH&RC) in Saudi Arabia. Experiments have shown that the proposed framework performs highly in detecting COVID infections by 96.9%. Its results can be output in minutes, which supports clinical staff in screening COVID-19 patients from their inpatient clinical data. © 2022 IEEE.

A prognostic model for classification of COVID-19 severity based on clinical and laboratory testing data

This study aimed to build an machine learning based model to predict the COVID-19 severity and reveal risk factors related to COVID-19 severity based on laboratory testing and clinical data for 420 participants, using tree-based models such as XGBoost, LightGBM, random forest. We calculated the Odds Ratios (OR) to investigate whether the top-ranked features were statistically significant for severity classification, turning out that high sensitivity C-reactive protein (hs-CRP) was the most important feature for determining of COVID-19 severity and XGBoost model showed the highest performance in classifying COVID-19 severity and healthy controls with F1score (0.84) and AUC (0.87). We expect that our results are of considerable significance for early screening for diagnosing COVID-19 severity, which, in turn, assist in further retrospective research for uncommon infectious diseases. © 2023 IEEE.

Active regression model for clinical grading of COVID-19.

Background: In the therapeutic process of COVID-19, the majority of indicators that physicians have for assisting treatment have come from clinical tests represented by proteins, metabolites, and immune levels in patients' blood. Therefore, this study constructs an individualized treatment model based on deep learning methods, aiming to realize timely intervention based on clinical test indicator data of COVID-19 patients and provide an important theoretical basis for optimizing medical resource allocation. Methods: This study collected clinical data from a total of 1,799 individuals, including 560 controls for non-respiratory infectious diseases (Negative), 681 controls for other respiratory virus infections (Other), and 558 coronavirus infections (Positive) for COVID-19. We first used the Student T-test to screen for statistically significant differences (Pvalue<0.05); we then used the Adaptive-Lasso method stepwise regression to screen the characteristic variables and filter the features with low importance; we then used analysis of covariance to calculate the correlation between variables and filter the highly correlated features; and finally, we analyzed the feature contribution and screened the best combination of features. Results: Feature engineering reduced the feature set to 13 feature combinations. The correlation coefficient between the projected results of the artificial intelligence-based individualized diagnostic model and the fitted curve of the actual values in the test group was 0.9449 which could be applied to the clinical prognosis of COVID-19. In addition, the depletion of platelets in patients with COVID-19 is an important factor affecting their severe deterioration. With the progression of COVID-19, there is a slight decrease in the total number of platelets in the patient's body, particularly as the volume of larger platelets sharply decreases. The importance of plateletCV (count*mean platelet volume) in evaluating the severity of COVID-19 patients is higher than the count of platelets and mean platelet volume. Conclusion: In general, we found that for patients with COVID-19, the increase in mean platelet volume was a predictor for SARS-Cov-2. The rapid decrease of platelet volume and the decrease of total platelet volume are dangerous signals for the aggravation of SARS-Cov-2 infection. The analysis and modeling results of this study provide a new perspective for individualized accurate diagnosis and treatment of clinical COVID-19 patients.

Survival Analysis and Its Application of Discharge Time Likelihood Prediction Using Clinical Data on COVID-19 Patients-Machine-Learning Approaches

COVID-19 has significant fatality rate since its appearance in December 2019 as a respiratory ailment that is extremely contagious. As the number of cases in reduction zones rises, highly health officials are control that authorized treatment centers may become overrun with corona virus patients. Artificial neural networks (ANNs) are machine coding that can be used to find complicate relationships between datasets. They enable the detection of category in complicated biological datasets that would be impossible to identify with traditional linear statistical analysis. To study the survival characteristics of patients, several computational techniques are used. Men and older age groups had greater mortality rates than women, according to this study. COVID-19 patients discharge times were predicted;also, utilizing various machine learning and statistical tools applied technically. In medical research, survival analysis is a regularly used technique for identifying relevant predictors of adverse outcomes and developing therapy guidelines for patients. Historically, demographic statistics have been used to predict outcomes in such patients. These projections, on the other hand, have little meaning for the individual patient. We present the training of neural networks to predict outcomes for individual patients at one institution, as well as their predictive performance using data from another institution in a different region. The research output show that the Gradient boosting longevity model beats the all other different models, also in this research study for predicting patient longevity. This study aims to assist health officials in making more informed decisions during the outbreak. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Therapeutic Effects of Corticosteroids for Critical and Severe COVID-19 Patients

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has led to an unprecedented public health, economic, and social crisis worldwide. Since no therapeutic treatment is yet available to effectively clear the virus and terminate transmission, supportive therapy is the primary clinical approach for coronavirus disease (COVID-19). The role of corticosteroids as one of the main means of anti-inflammatory adjuvants in the treatment of COVID-19 is controversial. Here, we retrospectively evaluated the therapeutic effects of corticosteroids by comparing clinical data of patients treated with or without a corticosteroids therapy at different severity levels. Kaplan-Meier curves shows that therapy with methylprednisolone and cortico-steroids increases the risk of death in patients with critical COVID-19 pneumonia. For patients in the critical group, the risk of death was slightly higher in males receiving corticosteroids therapy, while hypertension and trauma history reduced the hazard ratio. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Fusing clinical and image data for detecting the severity level of hospitalized symptomatic COVID-19 patients using hierarchical model

Purpose: Based on medical reports, it is hard to find levels of different hospitalized symptomatic COVID-19 patients according to their features in a short time. Besides, there are common and special features for COVID-19 patients at different levels based on physicians' knowledge that make diagnosis difficult. For this purpose, a hierarchical model is proposed in this paper based on experts' knowledge, fuzzy C-mean (FCM) clustering, and adaptive neuro-fuzzy inference system (ANFIS) classifier. Methods: Experts considered a special set of features for different groups of COVID-19 patients to find their treatment plans. Accordingly, the structure of the proposed hierarchical model is designed based on experts' knowledge. In the proposed model, we applied clustering methods to patients' data to determine some clusters. Then, we learn classifiers for each cluster in a hierarchical model. Regarding different common and special features of patients, FCM is considered for the clustering method. Besides, ANFIS had better performances than other classification methods. Therefore, FCM and ANFIS were considered to design the proposed hierarchical model. FCM finds the membership degree of each patient's data based on common and special features of different clusters to reinforce the ANFIS classifier. Next, ANFIS identifies the need of hospitalized symptomatic COVID-19 patients to ICU and to find whether or not they are in the end-stage (mortality target class). Two real datasets about COVID-19 patients are analyzed in this paper using the proposed model. One of these datasets had only clinical features and another dataset had both clinical and image features. Therefore, some appropriate features are extracted using some image processing and deep learning methods. Results: According to the results and statistical test, the proposed model has the best performance among other utilized classifiers. Its accuracies based on clinical features of the first and second datasets are 92% and 90% to find the ICU target class. Extracted features of image data increase the accuracy by 94%. Conclusion: The accuracy of this model is even better for detecting the mortality target class among different classifiers in this paper and the literature review. Besides, this model is compatible with utilized datasets about COVID-19 patients based on clinical data and both clinical and image data, as well. Highlights: • A new hierarchical model is proposed using ANFIS classifiers and FCM clustering method in this paper. Its structure is designed based on experts' knowledge and real medical process. FCM reinforces the ANFIS classification learning phase based on the features of COVID-19 patients. • Two real datasets about COVID-19 patients are studied in this paper. One of these datasets has both clinical and image data. Therefore, appropriate features are extracted based on its image data and considered with available meaningful clinical data. Different levels of hospitalized symptomatic COVID-19 patients are considered in this paper including the need of patients to ICU and whether or not they are in end-stage. • Well-known classification methods including case-based reasoning (CBR), decision tree, convolutional neural networks (CNN), K-nearest neighbors (KNN), learning vector quantization (LVQ), multi-layer perceptron (MLP), Naive Bayes (NB), radial basis function network (RBF), support vector machine (SVM), recurrent neural networks (RNN), fuzzy type-I inference system (FIS), and adaptive neuro-fuzzy inference system (ANFIS) are designed for these datasets and their results are analyzed for different random groups of the train and test data;• According to unbalanced utilized datasets, different performances of classifiers including accuracy, sensitivity, specificity, precision, F-score, and G-mean are compared to find the best classifier. ANFIS classifiers have the best results for both datasets. • To reduce the computational time, the effects of the Principal Component Analysis (PCA) feature reduction method are studied on th performances of the proposed model and classifiers. According to the results and statistical test, the proposed hierarchical model has the best performances among other utilized classifiers. Graphical : [Figure not available: see fulltext.] © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering.

Survival prediction of COVID-19 patients using multi-modal dataset

COVID-19 has proved to be a global emergency that has fractured the healthcare systems to the extent that its impact is too challenging to encompass. Though many Computer-Aided Diagnoses (CAD) systems have been developed for automatic detection of COVID-19 from Chest X-rays and chest CT images, very few works have been done on detecting COVID-19 from a clinical dataset. Resources needed for obtaining Clinical data like blood pressure, liver disease, past traveling history, etc., are inexpensive compared to collecting Chest CT images for COVID-19 infected patients. We propose a novel multi-model dataset for the survival prediction of patients infected with COVID-19. The dataset proposed is collected and created at Mahatma Gandhi Memorial Medical College, Indore. The dataset contains clinical data and chest X-ray images obtained from the same patient infected with COVID-19. For proper prognosis of the COVID19 positive patients from the clinical dataset, we have proposed a Bi-Stream Gated Attention-based CNN (BSGA-CNN) model. The BSGA-CNN model achieved an accuracy of 96.90% (± 3.05%). A CNN based on pre-trained VGG-Net is used to classify the corresponding Chest X-Ray images. It gave an accuracy of 87.76% (± 8.78%)%. © 2022 IEEE.

Predicting, Analyzing and Communicating Outcomes of COVID-19 Hospitalizations with Medical Images and Clinical Data

We propose PACO, a visual analytics framework to support the prediction, analysis, and communication of COVID-19 hospitalization outcomes. Although several real-world data sets about COVID-19 are openly available, most of the current research focuses on the detection of the disease. Until now, no previous work exists on combining insights from medical image data with knowledge extracted from clinical data, predicting the likelihood of an intensive care unit (ICU) visit, ventilation, or decease. Moreover, available literature has not yet focused on communicating such results to the broader society. To support the prediction, analysis and communication of the outcomes of COVID-19 hospitalizations on the basis of a publicly available data set comprising both electronic health data and medical image data [SSP∗21], we conduct the following three steps: (1) automated segmentation of the available X-ray images and processing of clinical data, (2) development of a model for the prediction of disease outcomes and a comparison to state-of-the-art prediction scores for both data sources, i.e., medical images and clinical data, and (3) the communication of outcomes to two different groups (i.e., clinical experts and the general population) through interactive dashboards. Preliminary results indicate that the prediction, analysis and communication of hospitalization outcomes is a significant topic in the context of COVID-19 prevention. © 2022 The Author(s) © 2022 The Eurographics Association.

Role of the Clinical Data Managers During the COVID-19 Pandemic: An Overview.

BACKGROUND: The COVID-19 pandemic has significantly changed the implementation of clinical trials. A large focus has been directed on clinical trial design, timeline, and best practices. It has led clinical trial study teams to update the existing processes and perform a risk assessment to mitigate the impact of the COVID-19 pandemic according to ICH-GCP (Good Clinical Practice) requirements. Data management plays a crucial role in understanding the study team's needs and developing innovative solutions. The Clinical Data Manager (CDM) is a core clinical trial Study Team member, responsible for promptly collecting, managing, and delivering complete, highquality data. OBJECTIVE: The COVID-19 pandemic required the Clinical Data Manager (CDM) to respond to changing needs by adapting data collection tools, data review strategies, and data management processes to answer new questions and address new challenges. CDMs became responsible for identifying how the COVID-19 pandemic impacted current data management processes and documentation and implementing changes to reflect new ways of working. The present article reviews the impact of the COVID-19 pandemic on clinical trials and the solutions adopted by the Clinical Data manager. CONCLUSION: The collection of COVID-19-related data points provides a better understanding of patient safety during the pandemic and proactively fulfills the growing regulatory interests. Strategies and innovative solutions adopted by the Clinical Data Manager serve as guidance for the clinical research team during the crisis to make the trials more robust and patient-centered.

Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models.

COVID-19 is a global threat for the healthcare systems due to the rapid spread of the pathogen that causes it. In such situation, the clinicians must take important decisions, in an environment where medical resources can be insufficient. In this task, the computer-aided diagnosis systems can be very useful not only in the task of supporting the clinical decisions but also to perform relevant analyses, allowing them to understand better the disease and the factors that can identify the high risk patients. For those purposes, in this work, we use several machine learning algorithms to estimate the outcome of COVID-19 patients given their clinical information. Particularly, we perform 2 different studies: the first one estimates whether the patient is at low or at high risk of death whereas the second estimates if the patient needs hospitalization or not. The results of the analyses of this work show the most relevant features for each studied scenario, as well as the classification performance of the considered machine learning models. In particular, the XGBoost algorithm is able to estimate the need for hospitalization of a patient with an AUC-ROC of 0 . 8415 ± 0 . 0217 while it can also estimate the risk of death with an AUC-ROC of 0 . 7992 ± 0 . 0104 . Results have demonstrated the great potential of the proposal to determine those patients that need a greater amount of medical resources for being at a higher risk. This provides the healthcare services with a tool to better manage their resources.

A wastewater-based risk index for SARS-CoV-2 infections among three cities on the Canadian Prairie.

Wastewater surveillance (WWS) is useful to better understand the spreading of coronavirus disease 2019 (COVID-19) in communities, which can help design and implement suitable mitigation measures. The main objective of this study was to develop the Wastewater Viral Load Risk Index (WWVLRI) for three Saskatchewan cities to offer a simple metric to interpret WWS. The index was developed by considering relationships between reproduction number, clinical data, daily per capita concentrations of virus particles in wastewater, and weekly viral load change rate. Trends of daily per capita concentrations of SARS-CoV-2 in wastewater for Saskatoon, Prince Albert, and North Battleford were similar during the pandemic, suggesting that per capita viral load can be useful to quantitatively compare wastewater signals among cities and develop an effective and comprehensible WWVLRI. The effective reproduction number (Rt) and the daily per capita efficiency adjusted viral load thresholds of 85 × 106 and 200 × 106 N2 gene counts (gc)/population day (pd) were determined. These values with rates of change were used to categorize the potential for COVID-19 outbreaks and subsequent declines. The weekly average was considered 'low risk' when the per capita viral load was 85 × 106 N2 gc/pd. A 'medium risk' occurs when the per capita copies were between 85 × 106 and 200 × 106 N2 gc/pd. with a rate of change <100 %. The start of an outbreak is indicated by a 'medium-high' risk classification when the week-over-week rate of change was >100 %, and the absolute magnitude of concentrations of viral particles was >85 × 106 N2 gc/pd. Lastly, a 'high risk' occurs when the viral load exceeds 200 × 106 N2 gc/pd. This methodology provides a valuable resource for decision-makers and health authorities, specifically given the limitation of COVID-19 surveillance based on clinical data.

Explainable AI Models for COVID-19 Diagnosis Using CT-Scan Images and Clinical Data

The pandemic of COVID-19 has had a significant impact on global health and is becoming a major international concern. Fortunately, early detection helped decrease its number of deaths. Artificial Intelligence (AI) and Machine Learning (ML) techniques are a new era, where the main objective is no longer to assist experts in decision-making but to improve and increase their capabilities and this is where interpretability comes in. This study aims to address one of the biggest hurdles that AI faces today which is public trust and acceptance due to its black-box strategy. In this paper, we use a deep Convolutional Neural Network (CNN) on chest computed tomography (CT) image data and Support Vector Machine (SVM) and Random Forest (RF) on clinical symptoms data (Bio-data) to diagnose patients positive for COVID-19. Our objective is to present an Explainable AI (XAI) models by using the Local Interpretable Model-agnostic Explanations (LIME) technique to identify positive patients to the virus in an interpreted way. The results are promising and outperformed the state of the art. The CNN model reached an Accuracy and F1-Score of 96% on CT-scan images, and SVM outperformed RF with Accuracy of 90% and Specificity of 91% on Bio-data. The interpretable results of XAI-Img-Model and XAI-Bio-Model, show that LIME explanations help to understand how SVM and CNN black box models behave in making their decision after being trained on different types of COVID-19 dataset. This can significantly increase trust and help experts understand and learn new patterns for the current pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Pneumonia and COVID-19 Detection in Chest X-rays Using Faster Region-Based Convolutional Neural Networks (Faster R-CNN)*

The arising of SARS-CoV-2 or 2019 novel coron-avirus in December 2019 have prioritized research on pulmonary diseases diagnosis and prognosis, especially using artificial intelligence (AI) and Deep Learning (DL). Polymerase Chain Reaction (PCR) is the most widely used technique to detect SARS-CoV-2, with a 0.12% false negative rate. While 75% of the hospitalized cases develop pneumonia caused by the virus, patients can still develop bacterial pneumonia. COVID-19 pneumonia can be diagnosed based on clinical data and Computed Tomography (CT scan). However, Chest X-rays are faster, cheaper, emit less radiations, and can be performed on bed-side. In this article, we extend the application of VGG-16 based Faster Region-Based Convolutional Neural Network (Faster R-CNN) to the detection of Pneumonia and COVID-19 in Chest X-ray images using several public datasets of total images count ranging from 2122 to 18455 Chest X-rays, and study the impact of several hyper-parameters such as objectness threshold and epochs count and length, to optimize the model's performance. Our results comply with the state of the art of Faster R-CNN in pneumonia detection as the best accuracy achieved is 65%. For COVID-19 detection, Faster R-CNN achieves a 90% validation accuracy. © 2022 IEEE.

Outcomes and risk factors with COVID-19 or influenza in hospitalized asthma patients.

BACKGROUND: At the time of the SARS-CoV-2 emergence, asthma patients were initially considered vulnerable because respiratory viruses, especially influenza, are associated with asthma exacerbations, increased risk of hospitalization and more severe disease course. We aimed to compare the asthma prevalence in patients hospitalized for COVID-19 or influenza and risk factors associated with poor prognosis with the diseases. METHODS: This retrospective cohort study used the Paris university hospitals clinical data warehouse to identify adults hospitalized for COVID-19 (January to June 2020) or influenza (November 2017 to March 2018 for the 2017-2018 influenza period and November 2018 to March 2019 for the 2018-2019 period). Asthma patients were identified with J45 and J46 ICD-10 codes. Poor outcomes were defined as admission in intensive care or death. RESULTS: Asthma prevalence was significantly higher among influenza than COVID-19 patients (n = 283/3 119, 9.1%, 95% CI [8.1-10.1] in 2017-2018 and n = 309/3 266, 9.5%, 95% CI [8.5-10.5] in 2018-2019 versus n = 402/9 009, 4.5%, 95% CI [4.0-4.9]). For asthma patients, 31% with COVID-19 were admitted to an intensive care unit versus 23% and 21% with influenza. Obesity was a risk factor for the 2017-2018 influenza period, smoking and heart failure for the 2018-2019 period. Among COVID-19 patients with asthma, smoking and obesity were risk factors for the severe form. CONCLUSIONS: In this study, patients with an asthma ICD-10 code were less represented among COVID-19 patients than among influenza-infected ones. However, outcomes were poorer for COVID-19 than influenza patients, both with asthma. These data highlight the importance of protective shields and vaccination against influenza and COVID-19 in this population.

Predictive and Prescriptive Analytics on Clinical Data using Improved K-NNtechnique

In recent years, the impactof pandemic situation there is a strong requirementoflateral thinking within the Healthcare sector. There is a consistent hesitancy for people to use hospital services because of social distancing, covid 19 circumstances, people prefer telemedicine system are rapidly increased. Health care provider provides with better treatment. Anartificial intelligence based dataanalytics shared by the patient through the IoT devices provides high security. In case of any emergencies Health care provider must be able to access the AI based Electronic Health records immediately and consuming less time in taking decisions before the patient could arrive. In this proposed improved KNN technique, preprocessing step is to produce an accurateoutput with additional efficient data and classification enhancement. The experimental results show that the improvedK-Nearest Neighbor (KNN) algorithm improves the accuracy and efficiency of classification.Apart from this, Trend analysis can be done to understand the various disease outbreaks which come under the data analytics.This experimental results of the proposed technique foradvanced data analytics in healthcaretreatment helps better treatment at low cost efficiently and effectively. © 2022 IEEE.

PERFORMANCE ANALYSIS OF DECISION TREE EXTENDED CLASSIFIERS (DTEC) ON CLINICAL DATASET OF COVID PREDICTION USING OPTIMAL FEATURE SELECTION

In Computer Science Technology, Artificial Intelligence, Data Analytics and Machine Learning plays a predominant role in decision-making strategies. By choosing suitable method and algorithms from any of these fields, a good decision can be taken. In health care industry, at present, prediction of Covid 19, is still a very big challenge, since false positive, false negative metrics are occurring frequently through various covid test. Our main aim, in this proposed work is to increase the prediction of accuracy of Covid, by considering the optimum number of features alone, by taking into consideration the laboratory measurement, and also by clinical test understanding. When target variable is binary, the classification algorithm based on Tree Based Extended Classifiers like Random Forest, AdaBoost, XGBoost can be proposed with necessary features. The results are observed from the proposed algorithms, that gets trained using the training dataset using standard data repository and it is being tested with the testing dataset. By analyzing the performance metrics, the obtained results showed that the prediction accuracy is increased and also false positive and false negative are reduced. In the proposed work, the tree based extended classifiers of Random Forest and Extended Gradient Boosting produces maximum 92% accuracy with 11 features using Gini Index. Apart from accuracy, the metrics such as false positive and false negative are playing the important role. In this proposed work, the false negative is as low as 5 out of 14 by XGB, and false positive with the minimum value of 3 out of 106 using Random Forest. Thus, these methods of covid predictions are useful for health care community, if it is being utilized in an efficient manner. © 2022 Little Lion Scientific.