A Unified Hierarchical XGBoost model for classifying priorities for COVID-19 vaccination campaign

Machine learning application in otology

This review presents a comprehensive history of Artificial Intelligence (AI) in the context of the revolutionary application of machine learning (ML) to medical research and clinical utilization, particularly for the benefit of researchers interested in the application of ML in otology. To this end, we discuss the key components of ML-input, output, and algorithms. In particular, some representation algorithms commonly used in medical research are discussed. Subsequently, we review ML applications in otology research, including diagnosis, influential identification, and surgical outcome prediction. In the context of surgical outcome prediction, specific surgical treatments, including cochlear implantation, active middle ear implantation, tympanoplasty, and vestibular schwannoma resection, are considered. Finally, we highlight the obstacles and challenges that need to be overcome in future research.

VirusPredictor: XGBoost-based software to predict virus-related sequences in human data

MOTIVATION

Discovering disease causative pathogens, particularly viruses without reference genomes, poses a technical challenge as they are often unidentifiable through sequence alignment. Machine learning prediction of patient high-throughput sequences unmappable to human and pathogen genomes may reveal sequences originating from uncharacterized viruses. Currently, there is a lack of software specifically designed for accurately predicting such viral sequences in human data.

RESULTS

We developed a fast XGBoost method and software VirusPredictor leveraging an in-house viral genome database. Our two-step XGBoost models first classify each query sequence into one of three groups: infectious virus, endogenous retrovirus (ERV) or non-ERV human. The prediction accuracies increased as the sequences became longer, i.e. 0.76, 0.93, and 0.98 for 150-350 (Illumina short reads), 850-950 (Sanger sequencing data), and 2000-5000 bp sequences, respectively. Then, sequences predicted to be from infectious viruses are further classified into one of six virus taxonomic subgroups, and the accuracies increased from 0.92 to >0.98 when query sequences increased from 150-350 to >850 bp. The results suggest that Illumina short reads should be de novo assembled into contigs (e.g. ∼1000 bp or longer) before prediction whenever possible. We applied VirusPredictor to multiple real genomic and metagenomic datasets and obtained high accuracies. VirusPredictor, a user-friendly open-source Python software, is useful for predicting the origins of patients' unmappable sequences. This study is the first to classify ERVs in infectious viral sequence prediction. This is also the first study combining virus sub-group predictions.

AVAILABILITY AND IMPLEMENTATION

www.dllab.org/software/VirusPredictor.html.

Stress begets stress: The moderating role of childhood adversity in the relationship between job stress and sleep quality among nurses

BACKGROUND

Nurses exhibit considerable variations in sleep quality and experience high job stress levels. Distal factors, such as childhood adversity, and proximal factors, both influence sleep quality. We investigated the moderating role of childhood adversity with job stress and sleep quality, and whether this aligns with the stress-sensitization or stress-amplification models.

METHODS

The impact of job stressors' total score and its dimensions on sleep quality was analyzed using traditional linear regression models and the extreme gradient boosting machine learning algorithm. The hierarchical regression examined the moderating role of childhood adversity in the relationship between job stress and sleep quality. An interactive tool was used to visualize the results.

RESULTS

Among the dimensions of job stress, "time allocation and workload" strongly correlated with sleep quality, followed by "nursing profession and work problems," "patient care issues," "management and interpersonal problems," and "working environment and equipment problems." The moderating role of childhood adversity in the relationship between different dimensions of job stressors (except working environment and equipment problems) and sleep quality aligns with the stress-sensitization model.

LIMITATIONS

This study was susceptible to recall bias and objective sleep data were unavailable. Cross-sectional study design was used, thus limiting causal inferences. Finally, the moderating effect of childhood adversity on subsequent stress among nurses remains unclear.

CONCLUSION

Childhood adversity and job stress were integrated into a stress-sensitization model, providing a nuanced and specific examination of sleep quality. Healthcare policymakers should focus on job stress and childhood adversity, improve nurses' sleep quality, and ultimately benefit patient care and outcomes.

Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting

Ensemble learning aims to improve prediction performance by combining several models or forecasts. However, how much and which ensemble learning techniques are useful in deep learning-based pipelines for pancreas computed tomography (CT) image classification is a challenge. Ensemble approaches are the most advanced solution to many machine learning problems. These techniques entail training multiple models and combining their predictions to improve the predictive performance of a single model. This article introduces the idea of Stacked Ensemble Deep Learning (SEDL), a pipeline for classifying pancreas CT medical images. The weak learners are Inception V3, VGG16, and ResNet34, and we employed a stacking ensemble. By combining the first-level predictions, an input train set for XGBoost, the ensemble model at the second level of prediction, is created. Extreme Gradient Boosting (XGBoost), employed as a strong learner, will make the final classification. Our findings showed that SEDL performed better, with a 98.8% ensemble accuracy, after some adjustments to the hyperparameters. The Cancer Imaging Archive (TCIA) public access dataset consists of 80 pancreas CT scans with a resolution of 512 * 512 pixels, from 53 male and 27 female subjects. A sample of two hundred and twenty-two images was used for training and testing data. We concluded that implementing the SEDL technique is an effective way to strengthen the robustness and increase the performance of the pipeline for classifying pancreas CT medical images. Interestingly, grouping like-minded or talented learners does not make a difference.

Development and validation of LightGBM algorithm for optimizing of Helicobacter pylori antibody during the minimum living guarantee crowd based gastric cancer screening program in Taizhou, China

Gastric cancer continues to be a significant health concern in China, with a high incidence rate. To mitigate its impact, early detection and treatment is key. However, conducting large-scale endoscopic gastric cancer screening is not feasible in China. Instead, a more appropriate approach would be to initially screen high-risk groups and follow up with endoscopic testing as needed. We conducted a study on 25,622 asymptomatic participants aged 45-70 years from a free gastric cancer screening program in the Taizhou city government's Minimum Living Guarantee Crowd (MLGC) initiative. Participants completed questionnaires, blood tests, and underwent gastrin-17 (G-17), pepsinogen I and II (PGI and PGII), and H. pylori IgG antibody (IgG) assessments. Using the light gradient boosting machine (lightGBM) algorithm, we developed a predictive model for gastric cancer risk. In the full model, F1 score was 2.66%, precision was 1.36%, and recall was 58.14%. In the high-risk model, F1 score was 2.51%, precision was 1.27%, and recall was 94.55%. Excluding IgG, the F1 score was 2.73%, precision was 1.40%, and recall was 68.62%. We conclude that H. pylori IgG appears to be able to be excluded from the prediction model without significantly affecting its performance, which is important from a health economic point of view. It suggests that screening indicators can be optimized, and expenditures reduced. These findings can have important implications for policymakers, as we can focus resources on other important aspects of gastric cancer prevention and control.

Identification and prediction of m7G-related Alzheimer's disease subtypes: insights from immune infiltration and machine learning models

Introduction

Alzheimer's disease (AD) is a complex and progressive neurodegenerative disorder that primarily affects older individuals. N7-methylguanosine (m7G) is a common RNA chemical modification that impacts the development of numerous diseases. Thus, our work investigated m7G-related AD subtypes and established a predictive model.

Methods

The datasets for AD patients, including GSE33000 and GSE44770, were obtained from the Gene Expression Omnibus (GEO) database, which were derived from the prefrontal cortex of the brain. We performed differential analysis of m7G regulators and examined the immune signatures differences between AD and matched-normal samples. Consensus clustering was employed to identify AD subtypes based on m7G-related differentially expressed genes (DEGs), and immune signatures were explored among different clusters. Furthermore, we developed four machine learning models based on the expression profiles of m7G-related DEGs and identified five important genes from the optimal model. We evaluated the predictive power of the 5-gene-based model using an external AD dataset (GSE44770).

Results

A total of 15 genes related to m7G were found to be dysregulated in patients with AD compared to non-AD patients. This finding suggests that there are differences in immune characteristics between these two groups. Based on the differentially expressed m7G regulators, we categorized AD patients into two clusters and calculated the ESTIMATE score for each cluster. Cluster 2 exhibited a higher ImmuneScore than Cluster 1. We performed the receiver operating characteristic (ROC) analysis to compare the performance of four models, and we found that the Random Forest (RF) model had the highest AUC value of 1.000. Furthermore, we tested the predictive efficacy of a 5-gene-based RF model on an external AD dataset and obtained an AUC value of 0.968. The nomogram, calibration curve, and decision curve analysis (DCA) confirmed the accuracy of our model in predicting AD subtypes.

Conclusion

The present study systematically examines the biological significance of m7G methylation modification in AD and investigates its association with immune infiltration characteristics. Furthermore, the study develops potential predictive models to assess the risk of m7G subtypes and the pathological outcomes of patients with AD, which can facilitate risk classification and clinical management of AD patients.

COVID-19 and Rumors: A Dynamic Nested Optimal Control Model

Unfortunately, the COVID-19 outbreak has been accompanied by the spread of rumors and depressing news. Herein, we develop a dynamic nested optimal control model of COVID-19 and its rumor outbreaks. The model aims to curb the epidemics by reducing the number of individuals infected with COVID-19 and reducing the number of rumor-spreaders while minimizing the cost associated with the control interventions. We use the modified approximation Karush-Kuhn-Tucker conditions with the Hamiltonian function to simplify the model before solving it using a genetic algorithm. The present model highlights three prevention measures that affect COVID-19 and its rumor outbreaks. One represents the interventions to curb the COVID-19 pandemic. The other two represent interventions to increase awareness, disseminate the correct information, and impose penalties on the spreaders of false rumors. The results emphasize the importance of interventions in curbing the spread of the COVID-19 pandemic and its associated rumor problems alike.

Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review

BACKGROUND

Social media and digital technologies have played essential roles in disseminating information and promoting vaccination during the COVID-19 pandemic. There is a need to summarize the applications and analytical techniques of social media and digital technologies in monitoring vaccine attitudes and administering COVID-19 vaccines.

OBJECTIVE

We aimed to synthesize the global evidence on the applications of social media and digital technologies in COVID-19 vaccination and to explore their avenues to promote COVID-19 vaccination.

METHODS

We searched 6 databases (PubMed, Scopus, Web of Science, Embase, EBSCO, and IEEE Xplore) for English-language articles from December 2019 to August 2022. The search terms covered keywords relating to social media, digital technology, and COVID-19 vaccines. Articles were included if they provided original descriptions of applications of social media or digital health technologies/solutions in COVID-19 vaccination. Conference abstracts, editorials, letters, commentaries, correspondence articles, study protocols, and reviews were excluded. A modified version of the Appraisal Tool for Cross-Sectional Studies (AXIS tool) was used to evaluate the quality of social media-related studies. The review was undertaken with the guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews.

RESULTS

A total of 178 articles were included in our review, including 114 social media articles and 64 digital technology articles. Social media has been applied for sentiment/emotion analysis, topic analysis, behavioral analysis, dissemination and engagement analysis, and information quality analysis around COVID-19 vaccination. Of these, sentiment analysis and topic analysis were the most common, with social media data being primarily analyzed by lexicon-based and machine learning techniques. The accuracy and reliability of information on social media can seriously affect public attitudes toward COVID-19 vaccines, and misinformation often leads to vaccine hesitancy. Digital technologies have been applied to determine the COVID-19 vaccination strategy, predict the vaccination process, optimize vaccine distribution and delivery, provide safe and transparent vaccination certificates, and perform postvaccination surveillance. The applied digital technologies included algorithms, blockchain, mobile health, the Internet of Things, and other technologies, although with some barriers to their popularization.

CONCLUSIONS

The applications of social media and digital technologies in addressing COVID-19 vaccination-related issues represent an irreversible trend. Attention should be paid to the ethical issues and health inequities arising from the digital divide while applying and promoting these technologies.

Uncovering the potential of innovation ecosystems in the healthcare sector after the COVID-19 crisis

Industry 4.0 technologies are expected to enhance healthcare quality at the minimum cost feasible by using innovative solutions based on a fruitful exchange of knowledge and resources among institutions, firms and academia. These collaborative mechanisms are likely to occur in an innovation ecosystem where different stakeholders and resources interact to provide ground-breaking solutions to the market. The paper proposes a framework for studying the creation and development of innovation ecosystems in the healthcare sector by using a set of interrelated dimensions including, technology, value, and capabilities within a Triple-Helix model guided by focal actors. The model is applied to an exemplary Italian innovation ecosystem providing cloud and artificial intelligence-based solutions to general practitioners (GPs) under the focal role of the Italian association of GPs. Primary and secondary data are examined starting from the innovation ecosystem's origins and continuing until the COVID-19 crisis. The findings show that the pandemic represented the turning point that altered the ecosystem's dimensions in order to find immediate solutions for monitoring health conditions and organizing the booking of swabs and vaccines. The data triangulation points out the technical, organizational, and administrative barriers hindering the widespread adoption of these solutions at the national and regional levels, revealing several implications for health policy.

Multi-texture features and optimized DeepNet for COVID-19 detection using chest x-ray images

The corona virus disease 2019 (COVID-19) pandemic has a severe influence on population health all over the world. Various methods are developed for detecting the COVID-19, but the process of diagnosing this problem from radiology and radiography images is one of the effective procedures for diagnosing the affected patients. Therefore, a robust and effective multi-local texture features (MLTF)-based feature extraction approach and Improved Weed Sea-based DeepNet (IWS-based DeepNet) approach is proposed for detecting the COVID-19 at an earlier stage. The developed IWS-based DeepNet is developed for detecting COVID-19to optimize the structure of the Deep Convolutional Neural Network (Deep CNN). The IWS is devised by incorporating the Improved Invasive Weed Optimization (IIWO) and Sea Lion Optimization (SLnO), respectively. The noises present in the input chest x-ray (CXR) image are discarded using Region of Interest (RoI) extraction by adaptive thresholding technique. For feature extraction, the proposed MLFT is newly developed by considering various texture features for extracting the best features. Finally, the COVID-19 detection is performed using the proposed IWS-based DeepNet. Furthermore, the proposed technique achieved effective performance in terms of True Positive Rate (TPR), True Negative Rate (TNR), and accuracy with the maximum values of 0.933%, 0.890%, and 0.919%.

A machine learning algorithm to analyse the effects of vaccination on COVID-19 mortality

The coronavirus disease 2019 (COVID-19), with new variants, continues to be a constant pandemic threat that is generating socio-economic and health issues in manifold countries. The principal goal of this study is to develop a machine learning experiment to assess the effects of vaccination on the fatality rate of the COVID-19 pandemic. Data from 192 countries are analysed to explain the phenomena under study. This new algorithm selected two targets: the number of deaths and the fatality rate. Results suggest that, based on the respective vaccination plan, the turnout in the participation in the vaccination campaign, and the doses administered, countries under study suddenly have a reduction in the fatality rate of COVID-19 precisely at the point where the cut effect is generated in the neural network. This result is significant for the international scientific community. It would demonstrate the effective impact of the vaccination campaign on the fatality rate of COVID-19, whatever the country considered. In fact, once the vaccination has started (for vaccines that require a booster, we refer to at least the first dose), the antibody response of people seems to prevent the probability of death related to COVID-19. In short, at a certain point, the fatality rate collapses with increasing doses administered. All these results here can help decisions of policymakers to prepare optimal strategies, based on effective vaccination plans, to lessen the negative effects of the COVID-19 pandemic crisis in socioeconomic and health systems.

Detection of COVID-19 using deep learning techniques and classification methods

Since the patient is not quarantined during the conclusion of the Polymerase Chain Reaction (PCR) test used in the diagnosis of COVID-19, the disease continues to spread. In this study, it was aimed to reduce the duration and amount of transmission of the disease by shortening the diagnosis time of COVID-19 patients with the use of Computed Tomography (CT). In addition, it is aimed to provide a decision support system to radiologists in the diagnosis of COVID-19. In this study, deep features were extracted with deep learning models such as ResNet-50, ResNet-101, AlexNet, Vgg-16, Vgg-19, GoogLeNet, SqueezeNet, Xception on 1345 CT images obtained from the radiography database of Siirt Education and Research Hospital. These deep features are given to classification methods such as Support Vector Machine (SVM), k Nearest Neighbor (kNN), Random Forest (RF), Decision Trees (DT), Naive Bayes (NB), and their performance is evaluated with test images. Accuracy value, F1-score and ROC curve were considered as success criteria. According to the data obtained as a result of the application, the best performance was obtained with ResNet-50 and SVM method. The accuracy was 96.296%, the F1-score was 95.868%, and the AUC value was 0.9821. The deep learning model and classification method examined in this study and found to be high performance can be used as an auxiliary decision support system by preventing unnecessary tests for COVID-19 disease.

Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh

Accurate predictive time series modelling is important in public health planning and response during the emergence of a novel pandemic. Therefore, the aims of the study are three-fold: (a) to model the overall trend of COVID-19 confirmed cases and deaths in Bangladesh; (b) to generate a short-term forecast of 8 weeks of COVID-19 cases and deaths; (c) to compare the predictive accuracy of the Autoregressive Integrated Moving Average (ARIMA) and eXtreme Gradient Boosting (XGBoost) for precise modelling of non-linear features and seasonal trends of the time series. The data were collected from the onset of the epidemic in Bangladesh from the Directorate General of Health Service (DGHS) and Institute of Epidemiology, Disease Control and Research (IEDCR). The daily confirmed cases and deaths of COVID-19 of 633 days in Bangladesh were divided into several training and test sets. The ARIMA and XGBoost models were established using those training data, and the test sets were used to evaluate each model's ability to forecast and finally averaged all the predictive performances to choose the best model. The predictive accuracy of the models was assessed using the mean absolute error (MAE), mean percentage error (MPE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The findings reveal the existence of a nonlinear trend and weekly seasonality in the dataset. The average error measures of the ARIMA model for both COVID-19 confirmed cases and deaths were lower than XGBoost model. Hence, in our study, the ARIMA model performed better than the XGBoost model in predicting COVID-19 confirmed cases and deaths in Bangladesh. The suggested prediction model might play a critical role in estimating the spread of a novel pandemic in Bangladesh and similar countries.

Machine Learning Solutions Applied to Amyotrophic Lateral Sclerosis Prognosis: A Review

The prognosis of Amyotrophic Lateral Sclerosis (ALS), a complex and rare disease, represents a challenging and essential task to better comprehend its progression and improve patients' quality of life. The use of Machine Learning (ML) techniques in healthcare has produced valuable contributions to the prognosis field. This article presents a systematic and critical review of primary studies that used ML applied to the ALS prognosis, searching for databases, relevant predictor biomarkers, the ML algorithms and techniques, and their outcomes. We focused on studies that analyzed biomarkers commonly present in the ALS disease clinical practice, such as demographic, clinical, laboratory, and imaging data. Hence, we investigate studies to provide an overview of solutions that can be applied to develop decision support systems and be used by a higher number of ALS clinical settings. The studies were retrieved from PubMed, Science Direct, IEEEXplore, and Web of Science databases. After completing the searching and screening process, 10 articles were selected to be analyzed and summarized. The studies evaluated and used different ML algorithms, techniques, datasets, sample sizes, biomarkers, and performance metrics. Based on the results, three distinct types of prediction were identified: Disease Progression, Survival Time, and Need for Support. The biomarkers identified as relevant in more than one study were the ALSFRS/ALSFRS-R, disease duration, Forced Vital Capacity, Body Mass Index, age at onset, and Creatinine. In general, the studies presented promissory results that can be applied in developing decision support systems. Besides, we discussed the open challenges, the limitations identified, and future research opportunities.

Application of machine learning in the fight against the COVID-19 pandemic: A review

Introduction: Machine learning (ML) plays a significant role in the fight against the COVID-19 (officially known as SARS-CoV-2) pandemic. ML techniques enable the rapid detection of patterns and trends in large datasets. Therefore, ML provides efficient methods to generate knowledge from structured and unstructured data. This potential is particularly significant when the pandemic affects all aspects of human life. It is necessary to collect a large amount of data to identify methods to prevent the spread of infection, early detection, reduction of consequences, and finding appropriate medicine. Modern information and communication technologies (ICT) such as the Internet of Things (IoT) allow the collection of large amounts of data from various sources. Thus, we can create predictive ML-based models for assessments, predictions, and decisions. Methods: This is a review article based on previous studies and scientifically proven knowledge. In this paper, bibliometric data from authoritative databases of research publications (Web of Science, Scopus, PubMed) are combined for bibliometric analyses in the context of ML applications for COVID-19. Aim: This paper reviews some ML-based applications used for mitigating COVID-19. We aimed to identify and review ML potentials and solutions for mitigating the COVID-19 pandemic as well as to present some of the most commonly used ML techniques, algorithms, and datasets applied in the context of COVID-19. Also, we provided some insights into specific emerging ideas and open issues to facilitate future research. Conclusion: ML is an effective tool for diagnosing and early detection of symptoms, predicting the spread of a pandemic, developing medicines and vaccines, etc.