Transparent and Accurate COVID-19 Diagnosis:Integrating Explainable AI with Advanced Deep Learning in CT Imaging

In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.Despite its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is imperative.To bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning models.This integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data augmentation.Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization.The pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s reasoning.This unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process.Our method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare systems.This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.

Anticoagulant use before COVID-19 diagnosis prevent COVID-19 associated acute venous thromboembolism or not:A systematic review and meta-analysis

BACKGROUND Coagulopathy and thromboembolic events are associated with poor outcomes in coronavirus disease 2019(COVID-19)patients.There is conflicting evidence on the effects of chronic anticoagulation on mortality and severity of COVID-19 disease.AIM To summarize the body of evidence on the effects of pre-hospital anticoagulation on outcomes in COVID-19 patients.METHODS A Literature search was performed on LitCovid PubMed,WHO,and Scopus databases from inception(December 2019)till June 2023 for original studies reporting an association between prior use of anticoagulants and patient outcomes in adults with COVID-19.The primary outcome was the risk of thromboembolic events in COVID-19 patients taking anticoagulants.Secondary outcomes included COVID-19 disease severity,in terms of intensive care unit admission or invasive mechanical ventilation/intubation requirement in patients hospitalized with COVID-19 infection,and mortality.The random effects models were used to calculate crude and adjusted odds ratios(aORs)with 95%confidence intervals(95%CIs).RESULTS Forty-six observational studies met our inclusion criteria.The unadjusted analysis found no association between prior anticoagulation and thromboembolic event risk[n=43851,9 studies,odds ratio(OR)=0.67(0.22,2.07);P=0.49;I2=95%].The association between prior anticoagulation and disease severity was non-significant[n=186782;22 studies,OR=1.08(0.78,1.49);P=0.64;I2=89%].However,pre-hospital anticoagulation significantly increased all-cause mortality risk[n=207292;35 studies,OR=1.72(1.37,2.17);P<0.00001;I2=93%].Pooling adjusted estimates revealed a statistically non-significant association between pre-hospital anticoagulation and thromboembolic event risk[aOR=0.87(0.42,1.80);P=0.71],mortality[aOR=0.94(0.84,1.05);P=0.31],and disease severity[aOR=0.96(0.72,1.26);P=0.76].CONCLUSION Prehospital anticoagulation was not significantly associated with reduced risk of thromboembolic events,improved survival,and lower disease severity in COVID-19 patients.

PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN

Since 2019,the coronavirus disease-19(COVID-19)has been spreading rapidly worldwide,posing an unignorable threat to the global economy and human health.It is a disease caused by severe acute respiratory syndrome coronavirus 2,a single-stranded RNA virus of the genus Betacoronavirus.This virus is highly infectious and relies on its angiotensin-converting enzyme 2-receptor to enter cells.With the increase in the number of confirmed COVID-19 diagnoses,the difficulty of diagnosis due to the lack of global healthcare resources becomes increasingly apparent.Deep learning-based computer-aided diagnosis models with high generalisability can effectively alleviate this pressure.Hyperparameter tuning is essential in training such models and significantly impacts their final performance and training speed.However,traditional hyperparameter tuning methods are usually time-consuming and unstable.To solve this issue,we introduce Particle Swarm Optimisation to build a PSO-guided Self-Tuning Convolution Neural Network(PSTCNN),allowing the model to tune hyperparameters automatically.Therefore,the proposed approach can reduce human involvement.Also,the optimisation algorithm can select the combination of hyperparameters in a targeted manner,thus stably achieving a solution closer to the global optimum.Experimentally,the PSTCNN can obtain quite excellent results,with a sensitivity of 93.65%±1.86%,a specificity of 94.32%±2.07%,a precision of 94.30%±2.04%,an accuracy of 93.99%±1.78%,an F1-score of 93.97%±1.78%,Matthews Correlation Coefficient of 87.99%±3.56%,and Fowlkes-Mallows Index of 93.97%±1.78%.Our experiments demonstrate that compared to traditional methods,hyperparameter tuning of the model using an optimisation algorithm is faster and more effective.

Covid-19 Diagnosis Using a Deep Learning Ensemble Model with Chest X-Ray Images

Covid-19 is a deadly virus that is rapidly spread around the world towards the end of the 2020.The consequences of this virus are quite frightening,especially when accompanied by an underlying disease.The novelty of the virus,the constant emergence of different variants and its rapid spread have a negative impact on the control and treatment process.Although the new test kits provide almost certain results,chest X-rays are extremely important to detect the progression and degree of the disease.In addition to the Covid-19 virus,pneumonia and harmless opacity of the lungs also complicate the diagnosis.Considering the negative results caused by the virus and the treatment costs,the importance of fast and accurate diagnosis is clearly seen.In this context,deep learning methods appear as an extremely popular approach.In this study,a hybrid model design with superior properties of convolutional neural networks is presented to correctly classify the Covid-19 disease.In addition,in order to contribute to the literature,a suitable dataset with balanced case numbers that can be used in all artificial intelligence classification studies is presented.With this ensemble model design,quite remarkable results are obtained for the diagnosis of three and four-class Covid-19.The proposed model can classify normal,pneumonia,and Covid-19 with 92.6%accuracy and 82.6%for normal,pneumonia,Covid-19,and lung opacity.

Optimum Threshold Estimation of Thorax CT Scan for COVID-19 Diagnosis in a Single Center in Cameroon

Objective: To evaluate the lung CT scan as a possible predictive diagnostic method for COVID-19 in the Cameroonian context. Methods: We designed a cross sectional study. Suspected cases of COVID-19 during the first wave at the national social insurance fund (NSIF) hospital were screened with both COVID-19 with lung CT scan and a PCR test. Univariate analysis was performed for sample description and multivariate analysis to assess the correlation between positive results for the PCR and other parameters. We estimated the optimum threshold of sensitivity/specificity, and area under curve using the empirical method and package. Results: A total of 62 suspected COVID-19 cases were recorded, predominantly males (Sex Ratio = 2.2) with a median age of 58.5 (IQR = 19.7). Among our 62 patients, 29 (46.8%) were confirmed COVID-19 cases with positive PCR results. All the patients had a thorax CT scan with a median impairment of 40% (IQR = 20%). The optimum threshold estimate for CT scan for COVID-19 infection diagnosis was 60% (95% CI = 25% - 80%). Overall, the sensitivity and specificity estimates were 0.30 (95% CI = 0.15 - 0.49) and 0.87 (95% CI = 0.70 - 0.96), respectively, leading to an Area Under Curve (AUC) estimate of 0.59 (95% CI = 0.46, 0.71). Conclusion: In this setting, lung CT scan was neither sensitive nor specific to predict COVID-19 disease.

Residual Attention Deep SVDD for COVID-19 Diagnosis Using CT Scans

COVID-19 is the common name of the disease caused by the novel coronavirus(2019-nCoV)that appeared in Wuhan,China in 2019.Discovering the infected people is the most important factor in the fight against the disease.The gold-standard test to diagnose COVID-19 is polymerase chain reaction(PCR),but it takes 5–6 h and,in the early stages of infection,may produce false-negative results.Examining Computed Tomography(CT)images to diagnose patients infected with COVID-19 has become an urgent necessity.In this study,we propose a residual attention deep support vector data description SVDD(RADSVDD)approach to diagnose COVID-19.It is a novel approach combining residual attention with deep support vector data description(DSVDD)to classify the CT images.To the best of our knowledge,we are the first to combine residual attention with DSVDD in general,and specifically in the diagnosis of COVID-19.Combining attention with DSVDD naively may cause model collapse.Attention in the proposed RADSVDD guides the network during training and enables quick learning,residual connectivity prevents vanishing gradients.Our approach consists of three models,each model is devoted to recognizing one certain disease and classifying other diseases as anomalies.These models learn in an end-to-end fashion.The proposed approach attained high performance in classifying CT images into intact,COVID-19,and non-COVID-19 pneumonia.To evaluate the proposed approach,we created a dataset from published datasets and had it assessed by an experienced radiologist.The proposed approach achieved high performance,with the normal model attained sensitivity(0.96–0.98),specificity(0.97–0.99),F1-score(0.97–0.98),and area under the receiver operator curve(AUC)0.99;the COVID-19 model attained sensitivity(0.97–0.98),specificity(0.97–0.99),F1-score(0.97–0.99),and AUC 0.99;and the non-COVID pneumoniamodel attained sensitivity(0.97–1),specificity(0.98–0.99),F1-score(0.97–0.99),and AUC 0.99.

Exploring Long COVID: Healthcare Utilization Up to 360 Days after a COVID-19 Diagnosis—Results from Analysis of a Very Large US National Sample

The topic of the long-term effects of COVID-19, so-called “long-COVID”, has gained increased attention. The US federal government announced plans to develop an interagency national research action plan to uncover more insights into the long-term effects of COVID-19. This study contributes to our understanding of the long-term effects of COVID-19 by quantifying patterns of healthcare utilization up to 360 days after an initial COVID-19 diagnosis occurring during the beginning of the pandemic (March-August 2020) in a very large nationally representative population of insured adults. We quantify actual COVID-19-related utilization (as opposed to reported symptoms) by accessing claims data to calculate average medical visits per patient per month by type of encounter (e.g. inpatient stay, physician visit). In contrast to many recent reports in the media, our results show that COVID-19-related utilization declines substantially after the first-month post-diagnosis and continues to decline throughout the study period to very low levels.

Correction to“Performance of a serological IgM and IgG qualitative test for COVID-19 diagnosis:An experimental study in Brazil”

Correction to“Freire de Melo F,Martins Oliveira Diniz L,Nélio Januário J,Fernando Gonçalves Ferreira J,Dórea RSDM,de Brito BB,Marques HS,Lemos FFB,Silva Luz M,Rocha Pinheiro SL,de Magalhães Queiroz DM.Performance of a serological IgM and IgG qualitative test for COVID-19 diagnosis:An experimental study in Brazil.World J Exp Med 2022;12(5):100-103[PMID:36196438 DOI:10.5493/wjem.v12.i5.100]”.In this article,we identified an issue with the“Acknowledgments”section.Here,we then provide a recognition section for our supporting institutions.

A Survey on Machine Learning in COVID-19 Diagnosis

Since Corona Virus Disease 2019 outbreak,many expert groups worldwide have studied the problem and proposed many diagnostic methods.This paper focuses on the research of Corona Virus Disease 2019 diagnosis.First,the procedure of the diagnosis based on machine learning is introduced in detail,which includes medical data collection,image preprocessing,feature extraction,and image classification.Then,we review seven methods in detail:transfer learning,ensemble learning,unsupervised learning and semi-supervised learning,convolutional neural networks,graph neural networks,explainable deep neural networks,and so on.What’smore,the advantages and limitations of different diagnosis methods are compared.Although the great achievements in medical images classification in recent years,Corona Virus Disease 2019 images classification based on machine learning still encountered many problems.For example,the highly unbalanced dataset,the difficulty of collecting labeled data,and thepoorqualityof thedata.Aiming at theseproblems,wepropose some solutions andprovide a comprehensive presentation for future research.

A Lightweight CNN Based on Transfer Learning for COVID-19 Diagnosis

The key to preventing the COVID-19 is to diagnose patients quickly and accurately.Studies have shown that using Convolutional Neural Networks(CNN)to analyze chest Computed Tomography(CT)images is helpful for timely COVID-19 diagnosis.However,personal privacy issues,public chest CT data sets are relatively few,which has limited CNN’s application to COVID-19 diagnosis.Also,many CNNs have complex structures and massive parameters.Even if equipped with the dedicated Graphics Processing Unit(GPU)for acceleration,it still takes a long time,which is not conductive to widespread application.To solve above problems,this paper proposes a lightweight CNN classification model based on transfer learning.Use the lightweight CNN MobileNetV2 as the backbone of the model to solve the shortage of hardware resources and computing power.In order to alleviate the problem of model overfitting caused by insufficient data set,transfer learning is used to train the model.The study first exploits the weight parameters trained on the ImageNet database to initialize the MobileNetV2 network,and then retrain the model based on the CT image data set provided by Kaggle.Experimental results on a computer equipped only with the Central Processing Unit(CPU)show that it consumes only 1.06 s on average to diagnose a chest CT image.Compared to other lightweight models,the proposed model has a higher classification accuracy and reliability while having a lightweight architecture and few parameters,which can be easily applied to computers without GPU acceleration.Code:github.com/ZhouJie-520/paper-codes.

Pseudo Zernike Moment and Deep Stacked Sparse Autoencoder for COVID-19 Diagnosis

(Aim)COVID-19 is an ongoing infectious disease.It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021.Traditional computer vision methods have achieved promising results on the automatic smart diagnosis.(Method)This study aims to propose a novel deep learning method that can obtain better performance.We use the pseudo-Zernike moment(PZM),derived from Zernike moment,as the extracted features.Two settings are introducing:(i)image plane over unit circle;and(ii)image plane inside the unit circle.Afterward,we use a deep-stacked sparse autoencoder(DSSAE)as the classifier.Besides,multiple-way data augmentation is chosen to overcome overfitting.The multiple-way data augmentation is based on Gaussian noise,salt-and-pepper noise,speckle noise,horizontal and vertical shear,rotation,Gamma correction,random translation and scaling.(Results)10 runs of 10-fold cross validation shows that our PZM-DSSAE method achieves a sensitivity of 92.06%±1.54%,a specificity of 92.56%±1.06%,a precision of 92.53%±1.03%,and an accuracy of 92.31%±1.08%.Its F1 score,MCC,and FMI arrive at 92.29%±1.10%,84.64%±2.15%,and 92.29%±1.10%,respectively.The AUC of our model is 0.9576.(Conclusion)We demonstrate“image plane over unit circle”can get better results than“image plane inside a unit circle.”Besides,this proposed PZM-DSSAE model is better than eight state-of-the-art approaches.

Covid-19 Diagnosis by Artificial Intelligence Based on Vibraimage Measurement of Behavioral Parameters

The hypothesis of behavioral parameters dependence measured from person’s head movements in quasi-stationary state on COVID-19 disease is discussed. Method for determining the dependence of vestibular-emotional reflex parameters on COVID-19, various diseases and pathologies are proposed. Micro-movements of a head for representatives of the control group (with a confirmed absence of COVID-19 disease) and a group of patients with a confirmed diagnosis of COVID-19 were studied using vibraimage technology. Parameters and criteria for the diagnosis of COVID-19 for training artificial intelligence (AI) on the control group and the patient group are proposed. 3-layer (one hidden layer) feedforward neural network (40 + 20 + 1 sigmoid neurons) was developed for AI training. AI was firstly trained on the primary sample of patients and a control group. Study of a random sample of people with trained AI was carried out and the possibility of detecting COVID-19 using the proposed method was proved a week before the onset of clinical symptoms of the disease. Number of COVID-19 diagnostic parameters was increased to 26 and AI was trained on a sample of 536 measurements, 268 patient measurement results and 268 measurement results in the control group. The achieved diagnostic accuracy was more than 99%, 4 errors per 536 measurements (2 false positive and 2 false negative), specificity 99.25% and sensitivity 99.25%. The issues of improving the accuracy and reliability of the proposed method for diagnosing COVID-19 are discussed. Further ways to improve the characteristics and applicability of the proposed method of diagnosis and self-diagnosis of COVID-19 are outlined.

Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

The novel Coronavirus disease 2019(COVID-19)pandemic has begun in China and is still affecting thousands of patient livesworldwide daily.AlthoughChest X-ray and Computed Tomography are the gold standardmedical imaging modalities for diagnosing potentially infected COVID-19 cases,applying Ultrasound(US)imaging technique to accomplish this crucial diagnosing task has attracted many physicians recently.In this article,we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images,based on generative adversarial neural networks(GANs).The proposed image classifiers are a semi-supervised GAN and a modifiedGANwith auxiliary classifier.Each one includes a modified discriminator to identify the class of the US image using semi-supervised learning technique,keeping its main function of defining the“realness”of tested images.Extensive tests have been successfully conducted on public dataset of US images acquired with a convex US probe.This study demonstrated the feasibility of using chest US images with two GAN classifiers as a new radiological tool for clinical check of COVID-19 patients.The results of our proposed GAN models showed that high accuracy values above 91.0%were obtained under different sizes of limited training data,outperforming other deep learning-based methods,such as transfer learning models in the recent studies.Consequently,the clinical implementation of our computer-aided diagnosis of US-COVID-19 is the future work of this study.

Performance of a serological IgM and IgG qualitative test for COVID-19 diagnosis:An experimental study in Brazil

Qualitative antibody tests are an easy,point-of-care diagnostic method that is useful in diagnosing coronavirus disease 2019,especially in situations where reverse transcription-polymerase chain reaction is negative.However,some factors are able to affect its sensitivity and accuracy,which may contribute to these tests not being used as a first-line diagnostic tool.

A Case of Investigation and Diagnosis of Immune Thrombocytopenic Purpura After Vaccination of COVID-19 Inactivated Vaccine

Objective:Analyze the relationship between inoculating one case of the COVID-19 inactivated vaccine(Vero cell)and immune thrombocytopenic purpura to provide a reference for the standardized handling of adverse events following immunization.Methods:According to the"National Monitoring Program for Suspected Adverse Reactions to Vaccinations,"an on-site investigation,data collection and analysis,expert group diagnosis,and medical association assessment were conducted on a case of immune thrombocytopenic purpura in District A of Chongqing after vaccination with the inactivated COVID-19 vaccine.The assessment report was delivered to the three relevant parties,the case was reviewed,and the experience was summarized.Results:The investigation and diagnosis by the district-level vaccination abnormal reaction expert group concluded that the disease that occurred after vaccination with the COVID-19 inactivated vaccine was secondary immune thrombocytopenic purpura,an abnormal reaction to the vaccination.The medical damage was classified as Level II Grade B.The vaccine production enterprise raised objections to this conclusion.After re-assessment by the municipal-level medical association,the conclusion was consistent with that of the district-level medical association.The vaccine production enterprise did not raise any further objections.Conclusion:Through active collaboration among district and municipal-level medical associations,disease control institutions,and vaccination units,the recipients have been promptly and effectively treated,providing financial support for their subsequent treatment and safeguarding their rights.The investigation and disposal procedures for adverse events following immunization in Chongqing are clear,and the mechanism is sound.It is necessary to continue strengthening the monitoring of adverse events following immunization according to the existing plan and to ensure timely and standardized handling.Simultaneously,it is crucial to strengthen vaccine management and vaccination management.

Application of microfluidic technologies on COVID-19 diagnosis and drug discovery

The ongoing coronavirus disease 2019(COVID-19) pandemic has boosted the development of antiviral research.Microfluidic technologies offer powerful platforms for diagnosis and drug discovery for severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) diagnosis and drug discovery.In this review,we introduce the structure of SARS-CoV-2 and the basic knowledge of microfluidic design.We discuss the application of microfluidic devices in SARS-CoV-2 diagnosis based on detecting viral nucleic acid,antibodies,and antigens.We highlight the contribution of lab-on-a-chip to manufacturing point-ofcare equipment of accurate,sensitive,low-cost,and user-friendly virus-detection devices.We then investigate the efforts in organ-on-a-chip and lipid nanoparticles(LNPs) synthesizing chips in antiviral drug screening and mRNA vaccine preparation.Microfluidic technologies contribute to the ongoing SARSCoV-2 research efforts and provide tools for future viral outbreaks.

Diagnosis,treatment protocols,and outcomes of liver transplant recipients infected with COVID-19

Several cases of fatal pneumonia during November 2019 were linked initially to severe acute respiratory syndrome coronavirus 2,which the World Health Organization later designated as coronavirus disease 2019(COVID-19).The World Health Organization declared COVID-19 as a pandemic on March 11,2020.In the general population,COVID-19 severity can range from asymptomatic/mild symptoms to seriously ill.Its mortality rate could be as high as 49%.The Centers for Disease Control and Prevention have acknowledged that people with specific underlying medical conditions,among those who need immunosuppression after solid organ transplantation(SOT),are at an increased risk of developing severe illness from COVID-19.Liver transplantation is the second most prevalent SOT globally.Due to their immunosuppressed state,liver transplant(LT)recipients are more susceptible to serious infections.Therefore,comorbidities and prolonged immunosuppression among SOT recipients enhance the likelihood of severe COVID-19.It is crucial to comprehend the clinical picture,immunosuppressive management,prognosis,and prophylaxis of COVID-19 infection because it may pose a danger to transplant recipients.This review described the clinical and laboratory findings of COVID-19 in LT recipients and the risk factors for severe disease in this population group.In the following sections,we discussed current COVID-19 therapy choices,reviewed standard practice in modifying immunosuppressant regimens,and outlined the safety and efficacy of currently licensed drugs for inpatient and outpatient management.Additionally,we explored the clinical outcomes of COVID-19 in LT recipients and mentioned the efficacy and safety of vaccination use.

Advances of CRISPR-Cas13 system in COVID-19 diagnosis and treatment

The ongoing global pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in over 570 million infections and 6 million deaths worldwide. Early detection and quarantine are essential to arrest the spread of the highly contagious COVID-19. High-risk groups, such as older adults and individuals with comorbidities, can present severe symptoms, including pyrexia, pertussis, and acute respiratory distress syndrome, on SARS-CoV-2 infection that can prove fatal, demonstrating a clear need for high-throughput and sensitive platforms to detect and eliminate SARS-CoV-2. CRISPR-Cas13, an emerging CRISPR system targeting RNA with high specificity and efficiency, has recently drawn much attention for COVID-19 diagnosis and treatment. Here, we summarized the current research progress on CRISPR-Cas13 in COVID-19 diagnosis and treatment and highlight the challenges and future research directions of CRISPR-Cas13 for effectively counteracting COVID-19.

Impact of COVID-19 on Biological Diagnosis of Malaria: Case of the Thierno Mouhamadoul Mansour Barro Hospital in Mbour, Senegal

Background: In Africa, malaria-endemic regions have not been spared from COVID-19 outbreak which emerged in the first quarter of 2020. This pandemic has shown clinical and therapeutic similarities with malaria. This following study sought to determine the impact of COVID-19 on the malaria diagnosis. Method: A review of laboratory registers and an exploitation of the District Health Information Software 2 (DHIS2) to collect information on the diagnosis of malaria by microscopy and by rapid diagnostic test (RDT), but also that of COVID-19 was done from 2017 to 2021 at the Thierno Mouhamadoul Mansour Hospital in Mbour, Senegal. Results: In 2017, 199 Thick drops (TDs) and 1852 RDTs were performed for malaria diagnosis. In 2018, it was 2352 malaria tests with 2138 RDTs and 214 TDs, before reaching a peak of 3943 tests in 2019 including 3742 RDTs and 201 TDs. By 2020, 2263 tests were performed with 2097 malaria RDTs, 158 TDs and 8 COVID RDTs. The latter increased significantly in 2021, reaching 444 COVID RDTs, while TDs and malaria RDT kept decreasing to 147 and 1036 respectively. Positive TDs were higher in 2020 (11.4%) compared to 2017 (3.5%), 2018 (1.4%), 2019 (6.5%) and 2021 (6.8%). For malaria RDTs, a decrease in the number of positive tests was noted between 2017 (4.5%) and 2021 (1.3%). The COVID RDTs were all negative in 2020, 29.5% were positive and 4.1% were undetermined in 2021. Conclusion: COVID-19 has led to changes in efforts to diagnose malaria as well as an increase in malaria prevalence directed towards children under 5 years of age.

Automatic Diagnosis of COVID-19 Patients from Unstructured Data Based on a Novel Weighting Scheme

The extraction of features fromunstructured clinical data of Covid-19 patients is critical for guiding clinical decision-making and diagnosing this viral disease.Furthermore,an early and accurate diagnosis of COVID-19 can reduce the burden on healthcare systems.In this paper,an improved Term Weighting technique combined with Parts-Of-Speech(POS)Tagging is proposed to reduce dimensions for automatic and effective classification of clinical text related to Covid-19 disease.Term Frequency-Inverse Document Frequency(TF-IDF)is the most often used term weighting scheme(TWS).However,TF-IDF has several developments to improve its drawbacks,in particular,it is not efficient enough to classify text by assigning effective weights to the terms in unstructured data.In this research,we proposed a modification term weighting scheme:RTF-C-IEF and compare the proposed model with four extraction methods:TF,TF-IDF,TF-IHF,and TF-IEF.The experiment was conducted on two new datasets for COVID-19 patients.The first datasetwas collected from government hospitals in Iraq with 3053 clinical records,and the second dataset with 1446 clinical reports,was collected from several different websites.Based on the experimental results using several popular classifiers applied to the datasets of Covid-19,we observe that the proposed scheme RTF-C-IEF achieves is a consistent performer with the best scores in most of the experiments.Further,the modifiedRTF-C-IEF proposed in the study outperformed the original scheme and other employed term weighting methods in most experiments.Thus,the proper selection of term weighting scheme among the different methods improves the performance of the classifier and helps to find the informative term.