A Mask Detection Algorithm Based on Improved Yolov5s

Aiming at the severe form of new coronavirus epidemic prevention and control, a target detection algorithm is proposed to detect whether masks are worn in public places. The Ghostnet and SElayer modules with fewer design parameters replace the BottleneckCSP part in the original Yolov5s network, which reduces the computational complexity of the model and improves the detection accuracy. The bounding box regression loss function DIOU is optimized, the DGIOU loss function is used for bounding box regression, and the center coordinate distance between the two bounding boxes is considered to achieve a better convergence effect. In the feature pyramid, the depthwise separable convolution DW is used to replace the ordinary convolution, which further reduces the amount of parameters and reduces the loss of feature information caused by multiple convolutions. The experimental results show that compared with the yolov5s algorithm, the proposed method improves the mAP by 4.6% and the detection rate by 10.7 frame/s in the mask wearing detection. Compared with other mainstream algorithms, the improved yolov5s algorithm has better generalization ability and practicability. © 2022 ACM.

Design and Coverage Path Planning of a Disinfection Robot

Eliminating pathogen exposure is an important approach to control outbreaks of epidemics such as COVID-19 (coronavirus disease 2019). To deal with pathogenic environments, using disinfection robots is a practicable choice. This research formulates a 3D (three-dimensional) spatial disinfection strategy for a disinfection robot. First, a disinfection robot is designed with an extensible control framework for the integration of additional functions. The robot has eight degrees of freedom that can handle disinfection tasks in complex 3D environments where normal disinfection robots lack the capability to ensure complete disinfection. An ingenious clamping mechanism is designed to increase flexibility and adaptability. Secondly, a new coverage path planning algorithm targeted at the spraying area is used. This algorithm aims to achieve an optimal path via the rotating calipers algorithm after transformation between a 2D (two-dimensional) array and 3D space. Finally, the performance of the designed robot is tested through a series of simulations and experiments in various spaces that humans usually live in. The results demonstrate that the robot can effectively perform disinfection tasks both in computer simulation and in reality.

Monitoring and Early Warning of SMEs' Shutdown Risk under the Impact of Global Pandemic Shock

The COVID-19 outbreak devastated business operations and the world economy, especially for small and medium-sized enterprises (SMEs). With limited capital, poorer risk tolerance, and difficulty in withstanding prolonged crises, SMEs are more vulnerable to pandemics and face a higher risk of shutdown. This research sought to establish a model response to shutdown risk by investigating two questions: How do you measure SMEs' shutdown risk due to pandemics? How do SMEs reduce shutdown risk? To the best of our knowledge, existing studies only analyzed the impact of the pandemic on SMEs through statistical surveys and trivial recommendations. Particularly, there is no case study focusing on an elaboration of SMEs' shutdown risk. We developed a model to reduce cognitive uncertainty and differences in opinion among experts on COVID-19. The model was built by integrating the improved Dempster's rule of combination and a Bayesian network, where the former is based on the method of weight assignment and matrix analysis. The model was first applied to a representative SME with basic characteristics for survival analysis during the pandemic. The results show that this SME has a probability of 79% on a lower risk of shutdown, 15% on a medium risk of shutdown, and 6% of high risk of shutdown. SMEs solving the capital chain problem and changing external conditions such as market demand are more difficult during a pandemic. Based on the counterfactual elaboration of the inferred results, the probability of occurrence of each risk factor was obtained by simulating the interventions. The most likely causal chain analysis based on counterfactual elaboration revealed that it is simpler to solve employee health problems. For the SMEs in the study, this approach can reduce the probability of being at high risk of shutdown by 16%. The results of the model are consistent with those identified by the SME respondents, which validates the model.

COVID-19 Image Segmentation Algorithms Based on Conditional Generative Adversarial Network

The size and location of the lesions in CT images of novel corona virus pneumonia (COVID-19) change all the time, and the lesion areas have low contrast and blurred boundaries, resulting in difficult segmentation. To solve this problem, a COVID-19 image segmentation algorithm based on conditional generative adversarial network (CGAN) is proposed. Uses the improved DeeplabV3+ network as a generator, which enhances the extraction of multi-scale contextual features, reduces the number of network parameters and improves the training speed. A Markov discriminator with 6 fully convolutional layers is proposed instead of a common discriminator, with the aim of focusing more on the local features of the CT image. By continuously adversarial training between the generator and the discriminator, the network weights are optimised so that the final segmented image generated by the generator is infinitely close to the ground truth. On the COVID-19 CT public dataset, the area under the curve of ROC, F1-Score and dice similarity coefficient achieved 96.64%, 84.15% and 86.14% respectively. The experimental results show that the proposed algorithm is accurate and robust, and it has the possibility of becoming a safe, inexpensive, and time-saving medical assistant tool in clinical diagnosis, which provides a reference for computer-aided diagnosis. © 2022 ACM.

A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model

Due to the inappropriate or untimely distribution of post-disaster goods, many regions did not receive timely and efficient relief for infected people in the coronavirus disease outbreak that began in 2019. This study develops a model for the emergency relief routing problem (ERRP) to distribute post-disaster relief more reasonably. Unlike general route optimizations, patients' suffering is taken into account in the model, allowing patients in more urgent situations to receive relief operations first. A new metaheuristic algorithm, the hybrid brain storm optimization (HBSO) algorithm, is proposed to deal with the model. The hybrid algorithm adds the ideas of the simulated annealing (SA) algorithm and large neighborhood search (LNS) algorithm into the BSO algorithm, improving its ability to escape from the local optimum trap and speeding up the convergence. In simulation experiments, the BSO algorithm, BSO+LNS algorithm (combining the BSO with the LNS), and HBSO algorithm (combining the BSO with the LNS and SA) are compared. The results of simulation experiments show the following: (1) The HBSO algorithm outperforms its rivals, obtaining a smaller total cost and providing a more stable ability to discover the best solution for the ERRP;(2) the ERRP model can greatly reduce the level of patient suffering and can prioritize patients in more urgent situations.

A qualitative study exploring how youth use TikTok to learn about mental health and mental health services in British Columbia, Canada

Aims: Youth are increasingly seeking health information through online platforms, such as websites, social media, and online forums. TikTok emerged as a popular platform for disseminating and consuming health information during the COVID-19 pandemic. As such, this study aimed to explore how youth used TikTok to access information about mental health and mental health services during the pandemic. Method(s): Twenty-one interviews were conducted over Zoom with youth (ages 12-24) who lived in British Columbia, Canada and had accessed TikTok for mental health information during the pandemic. Interviews were audio-recorded, transcribed verbatim, and analysed thematically using an inductive approach. Result(s): Youth described TikTok as a safe place to talk about mental health and share personal experiences. This helped youth feel less alone with their struggles and facilitated conversations about mental health with friends, family, and service providers. Participants also described how mental health content on TikTok helped them be more mindful of their own mental health and the different resources and coping strategies available and encouraged them to seek services. For those hesitant or unable to access services, TikTok provided immediate support. Youth appreciated the ease of accessing this information, given the platform's engaging and digestible format (i.e., short videos) and predictive nature of its algorithm. However, participants expressed concerns with the spread of misinformation and the lack of verifiable information on the platform. Conclusion(s): TikTok is as a practical platform to disseminate mental health information to youth. However, efforts to establish strategies for preventing and reporting misinformation are warranted.

Innovative Methods for Integrating Social Determinants of Health Data with Administrative Claims to Facilitate Health Equity Research

Objectives: The Covid-19 pandemic highlighted the importance of considering Social Determinants of Health (SDoH) in healthcare research. Administrative claims databases are widely used for research, but often lack SDoH data or sufficient transparency in how these data were obtained. This study describes innovative methods for integrating SDoH data with administrative claims to facilitate health equity research. Method(s): The HealthCore Integrated Research Database (HIRD) contains medical and pharmacy claims from a large, national US payer starting in 2006 and includes commercial (Comm), Medicare Advantage (MCare), and Medicaid (MCaid) populations. The HIRD includes individually identifiable information, which was used for linking with SDoH data from the following sources: national neighborhood-level data from the American Community Survey, the Food Access Research Atlas, and the National Center for Health Statistics' urbanicity classification;and member-level data on race/ethnicity from enrollment files, medical records, self-attestation, and imputation algorithms. We examined SDoH metrics for members enrolled as of 05-July-2022 and compared them to the respective US national data using descriptive statistics. We also examined telehealth utilization in 2022. Result(s): SDoH data were available for ~95% of currently active members in the HIRD (Comm/MCare/MCaid 12.5m/1m/7.6m). Socioeconomic characteristics at the neighborhood-level differed by membership type and vs. national data: % of members with at least a high-school education (90/88/84 vs. 87);median family income ($98k/$76k/$70k vs. $82k);% of members living in low-income low-food-access tracts (9/14/18 vs. 13);urban (57/52/47 vs. 61). At the member-level, the % of White Non-Hispanics, Black Non-Hispanics, Asian Non-Hispanics, and Hispanics were 61/6/5/6 (Comm), 76/12/2/2 (MCare), and 45/26/5/19 (MCaid). Imputation contributed 15-60% of race/ethnicity values across membership types. Telehealth utilization increased with socioeconomic status. Conclusion(s): We successfully integrated SDoH data from a variety of sources with administrative claims. SDoH characteristics differed by type of insurance coverage and were associated with differences in telehealth utilization.Copyright © 2023

The duration of symptoms relief after treatment in symptomatic COVID-19 cases

Purpose: The clinical profile of coronavirus disease (COVID-19) has a wide range of symptoms from self-limiting viral upper respiratory tract infection to death from arrest. The symptoms vary depending on the severity of the disease and countries. Experts from many parts of the world report on symptoms and onset times, but there are still many unanswered questions about the new disease, COVID-19. The prevalence of symptoms and, in particular, the relief durations are also questions that need to be answered. Moreover, there is no common algorithm for post-treatment follow-up in this disease, which can cause many organ damage. Materials and methods: The aim of this cross-sectional survey study is to find answers to these questions. A total of 185 symptomatic people, who were discharged after inpatient treatment in Elazig Fethi Sekin City Hospital in Turkey in December 2020, voluntarily participated in the study. Volunteers were asked questions about the duration of relief of symptoms after treatment. Each of the common symptoms was examined separately (16 questions). The data obtained were statistically analyzed using Microsoft Excel and SPSS program;and charted by using the Python 3.0 Seaborn library. Results: According to the answers of the participants, it was concluded that the symptoms could persist for more than 1 month and therefore they repeatedly applied to the hospital. Conclusion: The uncertainty about the symptoms and duration of COVID-19 after treatment imposes serious financial burdens on health organizations. Due to this reason, it is urgently necessary to conduct large-scale randomized studies and determine follow-up algorithms after treatment. © 2022, Pamukkale University. All rights reserved.

Improved mask detection based on YOLOv5s

COVID-19 epidemic is not over. The correct wearing of masks can effectively prevent the spread of the virus. Aiming at a series of problems of existing mask-wearing detection algorithms, such as only detecting whether to wear or not, being unable to detect whether to wear correctly, difficulty in detecting small targets in dense scenes, and low detection accuracy, It is suggested to use a better algorithm based on YOLOv5s. It improves the generalization and transmission performance of the model by changing the ACON activation function. Then Bifpn is used to replace PAN to effectively integrate the target features of different sizes extracted by the network. Finally, To enable the network to pay attention to a wide area, CA is introduced to the backbone. This embeds the location information into the channel attention. © 2023 SPIE.

Explainable Mortality Prediction Model for Congestive Heart Failure with Nature-Based Feature Selection Method

A mortality prediction model can be a great tool to assist physicians in decision making in the intensive care unit (ICU) in order to ensure optimal allocation of ICU resources according to the patient's health conditions. The entire world witnessed a severe ICU patient capacity crisis a few years ago during the COVID-19 pandemic. Various widely utilized machine learning (ML) models in this research field can provide poor performance due to a lack of proper feature selection. Despite the fact that nature-based algorithms in other sectors perform well for feature selection, no comparative study on the performance of nature-based algorithms in feature selection has been conducted in the ICU mortality prediction field. Therefore, in this research, a comparison of the performance of ML models with and without feature selection was performed. In addition, explainable artificial intelligence (AI) was used to examine the contribution of features to the decision-making process. Explainable AI focuses on establishing transparency and traceability for statistical black-box machine learning techniques. Explainable AI is essential in the medical industry to foster public confidence and trust in machine learning model predictions. Three nature-based algorithms, namely the flower pollination algorithm (FPA), particle swarm algorithm (PSO), and genetic algorithm (GA), were used in this study. For the classification job, the most widely used and diversified classifiers from the literature were used, including logistic regression (LR), decision tree (DT) classifier, the gradient boosting (GB) algorithm, and the random forest (RF) algorithm. The Medical Information Mart for Intensive Care III (MIMIC-III) dataset was used to collect data on heart failure patients. On the MIMIC-III dataset, it was discovered that feature selection significantly improved the performance of the described ML models. Without applying any feature selection process on the MIMIC-III heart failure patient dataset, the accuracy of the four mentioned ML models, namely LR, DT, RF, and GB was 69.9%, 82.5%, 90.6%, and 91.0%, respectively, whereas with feature selection in combination with the FPA, the accuracy increased to 71.6%, 84.8%, 92.8%, and 91.1%, respectively, for the same dataset. Again, the FPA showed the highest area under the receiver operating characteristic (AUROC) value of 83.0% with the RF algorithm among all other algorithms utilized in this study. Thus, it can be concluded that the use of feature selection with FPA has a profound impact on the outcome of ML models. Shapley additive explanation (SHAP) was used in this study to interpret the ML models. SHAP was used in this study because it offers mathematical assurances for the precision and consistency of explanations. It is trustworthy and suitable for both local and global explanations. It was found that the features that were selected by SHAP as most important were also most common with the features selected by the FPA. Therefore, we hope that this study will help physicians to predict ICU mortality for heart failure patients with a limited number of features and with high accuracy.

Research on cold chain logistics optimization model considering low-carbon emissions

Cold chain logistics distribution orders have increased due to the impact of COVID-19. In view of the increasing difficulty of route optimization and the increase of carbon emissions in the process of cold chain logistics distribution, a mathematical model for route optimization of cold chain logistics distribution vehicles with minimum comprehensive cost is established by considering the cost of carbon emission intensity comprehensively in this paper. The main contributions of this paper are as follows: 1) An improved hybrid ant colony algorithm is proposed, which combined simulated annealing algorithm to get rid of the local optimal solution. 2) Chaotic mapping is introduced in pheromone update to accelerate convergence and improve search efficiency. The effectiveness of the proposed method in optimizing cold chain logistics distribution path and reducing costs is verified by simulation experiments and comparison with the existing classical algorithms. © 2023 The Author(s). Published by Oxford University Press.

Masked Student Dataset of Expressions

Facial expression recognition (FER) algorithms work well in constrained environments with little or no occlusion of the face. However, real-world face occlusion is prevalent, most notably with the need to use a face mask in the current Covid-19 scenario. While there are works on the problem of occlusion in FER, little has been done before on the particular face mask scenario. Moreover, the few works in this area largely use synthetically created masked FER datasets. Motivated by these challenges posed by the pandemic to FER, we present a novel dataset, the Masked Student Dataset of Expressions or MSD-E, consisting of 1,960 real-world non-masked and masked facial expression images collected from 142 individuals. Along with the issue of obfuscated facial features, we illustrate how other subtler issues in masked FER are represented in our dataset. We then provide baseline results using ResNet-18, finding that its performance dips in the non-masked case when trained for FER in the presence of masks. To tackle this, we test two training paradigms: contrastive learning and knowledge distillation, and find that they increase the model's performance in the masked scenario while maintaining its non-masked performance. We further visualise our results using t-SNE plots and Grad-CAM, demonstrating that these paradigms capitalise on the limited features available in the masked scenario. Finally, we benchmark SOTA methods on MSD-E. The dataset is available at https://github.com/SridharSola/MSD-E. © 2022 ACM.

Multi-objective Evolutionary Discretization of Gene Expression Profiles: Application to COVID-19 Severity Prediction

Machine learning models can use information from gene expressions in patients to efficiently predict the severity of symptoms for several diseases. Medical experts, however, still need to understand the reasoning behind the predictions before trusting them. In their day-to-day practice, physicians prefer using gene expression profiles, consisting of a discretized subset of all data from gene expressions: in these profiles, genes are typically reported as either over-expressed or under-expressed, using discretization thresholds computed on data from a healthy control group. A discretized profile allows medical experts to quickly categorize patients at a glance. Building on previous works related to the automatic discretization of patient profiles, we present a novel approach that frames the problem as a multi-objective optimization task: on the one hand, after discretization, the medical expert would prefer to have as few different profiles as possible, to be able to classify patients in an intuitive way;on the other hand, the loss of information has to be minimized. Loss of information can be estimated using the performance of a classifier trained on the discretized gene expression levels. We apply one common state-of-the-art evolutionary multi-objective algorithm, NSGA-II, to the discretization of a dataset of COVID-19 patients that developed either mild or severe symptoms. The results show not only that the solutions found by the approach dominate traditional discretization based on statistical analysis and are more generally valid than those obtained through single-objective optimization, but that the candidate Pareto-optimal solutions preserve the sense-making that practitioners find necessary to trust the results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Social Economic Impacts for Covid-19 Pandemics Using Machine Learning Based Optimization Algorithm

As the number of COVID-19 patients grows exponentially, not all cases are likely dealt with by doctors and medical professionals. Researchers will add to the fight against COVID-19 by developing smarter strategies to achieve accelerated control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus that causes disease. Proposed method suggests best ways to optimize protection and avoid COVID-19 spread. Big benefit of the hybrid algorithm is that COVID-19 is diagnosed and treated more rapidly. Pandemic diseases possibilities are handling with help of Computational Intelligence, using cases and applications from current COVID-19 pandemic. This work discusses data that can be analyzed based on optimization algorithm which provides betterCOVID-19 detection and diagnosis. This algorithm uses a machine learning model to decide how the hazard function changes concerning characteristics of potential methods to find parameters in optimization of machine learning model, which has in many cases been shown to be accurate for actual clinical datasets. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Multivariate Conway-Maxwell-Poisson Distribution: Sarmanov Method and Doubly Intractable Bayesian Inference

In this article, a multivariate count distribution with Conway-Maxwell (COM)-Poisson marginals is proposed. To do this, we develop a modification of the Sarmanov method for constructing multivariate distributions. Our multivariate COM-Poisson (MultCOMP) model has desirable features such as (i) it admits a flexible covariance matrix allowing for both negative and positive nondiagonal entries;(ii) it overcomes the limitation of the existing bivariate COM-Poisson distributions in the literature that do not have COM-Poisson marginals;(iii) it allows for the analysis of multivariate counts and is not just limited to bivariate counts. Inferential challenges are presented by the likelihood specification as it depends on a number of intractable normalizing constants involving the model parameters. These obstacles motivate us to propose Bayesian inferential approaches where the resulting doubly intractable posterior is handled with via the noisy exchange algorithm or the Grouped Independence Metropolis–Hastings algorithm. Numerical experiments based on simulations are presented to illustrate the proposed Bayesian approach. We demonstrate the potential of the MultCOMP model through a real data application on the numbers of goals scored by the home and away teams in the English Premier League from 2018 to 2021. Here, our interest is to assess the effect of a lack of crowds during the COVID-19 pandemic on the well-known home team advantage. A MultCOMP model fit shows that there is evidence of a decreased number of goals scored by the home team, not accompanied by a reduced score from the opponent. Hence, our analysis suggests a smaller home team advantage in the absence of crowds, which agrees with the opinion of several football experts. Supplementary materials for this article are available online.

CovidXNet: An Ensemble of Deep Neural Networks for Non-invasive Binary Identification of COVID-19 through Chest Radiographs

This study compared five different image classification algorithms, namely VGG16, VGG19, AlexNet, DenseNet, and ConVNext, based on their ability to detect and classify COVID-19-related cases given chest X-ray images. Using performance metrics like accuracy, F1 score, precision, recall, and MCC compared these intelligent classification algorithms. Upon testing these algorithms, the accuracy for each model was quite unsatisfactory, ranging from 80.00% to 92.50%, provided it is for medical application. As such, an ensemble learning-based image classification model, made up of AlexNet and VGG19 called CovidXNet, was proposed to detect COVID-19 through chest X-ray images discriminating between health and pneumonic lung images. CovidXNet achieved an accuracy of 97.00%, which was significantly better considering past results. Further studies may be conducted to increase the accuracy, particularly for identifying and classifying chest radiographs for COVID-19-related cases, since the current model may still provide false negatives, which may be detrimental to the prevention of the spread of the virus. © 2022 IEEE.

Immunotherapy: RAPID ACCESS TO VIRUS-SPECIFIC T CELLS FOR ADOPTIVE IMMUNOTHERAPY FOR COMPASSIONATE USE IN AUSTRALIA

Background & Aim: Adoptive T cell immunotherapy holds great promise for the treatment of viral complications. Our group has been developing and trialling virus-specific T cell therapies for more than 20 years. Recently, we have generated a repository of multi-virus-specific T cells for our clinical trials. Unfortunately, for many patients with viral complications, there is no suitable trial through which to access these therapies. In Australia, the Therapeutic Goods Administration has a Special Access Scheme (SAS) to enable provision of unapproved therapies for compassionate use. Our research group is now a leading Australian provider of "off-the-shelf" and custom-grown allogeneic virus-specific T cells to hospitals for patients with no other treatment options. Methods, Results & Conclusion(s): We have generated a repository of multi-virus-specific T cells from 20 healthy donors, with up to 150 doses of T cells per donor generated from a single blood sample. Each product batch is thoroughly characterised in terms of viral antigen specificity, HLA restriction and alloreactivity. These T cells target a combination of Epstein-Barr virus, cytomegalovirus, BK polyomavirus, John Cunningham virus and adenovirus epitopes. We have also generated a repository of SARS-CoV-2-specific T cells and occasionally grow custom patient-specific batches of T cells from nominated donors, on request. Since 2008, we have provided virus-specific T cells to 15 hospitals across Australia, and the volume of supply requests has significantly increased in recent years, as clinicians have gained interest in adoptive immunotherapy. In 2022, we provided T cells for 26 patients via the SAS. The majority were experiencing post-transplant complications, including cytomegalovirus disease, BK virus-associated haemorrhagic cystitis and post-transplant lymphoproliferative disorder. Through our clinical trials, we have developed rigorous processes for T cell therapy manufacture and characterisation, in addition to a computer-based selection algorithm, which we apply to SAS cases. As these cases are not part of a clinical trial, concomitant therapy varies, and monitoring is not uniform. However, we have received reports of clinical benefit from adoptive T cell therapy. These include cases of reduction in viral load, improvement in symptoms, and complete resolution of infection. We believe that these promising T cell therapies should be available to hospitals through a nationally funded centre for cellular therapies for critically ill patients.Copyright © 2023 International Society for Cell & Gene Therapy

On the Frontline Against COVID-19: Provision of Community Pharmacy Services during a Public Health Crises

Objectives: During the current pandemic, it is recognised that pharmacies will often be the first point of contact with the health system for individuals with COVID-19 related health concerns or who require reliable information and advice. It is also important in the midst of the current public health crisis to reduce general practitioners' (GP) minor ailment-related workload. The aim of our study is to examine the problems in the midst of public health crisis of the current magnitude with the roles and activities of pharmacists. This information could help to inform future decisions about the restructuring of existing health services by governments, public health bodies and policy makers in response to public health crises such as COVID-19. Method(s): The study was carried out among 384 consumers using pharmacy in the regions of Armenia and Yerevan. Research instrument was questionnaire. Number of questionnaires distribution was determined by The Survey System Version 11.0. Analyses were performed using Statistical Package for the Social Sciences (SPSS) software (version 12.0). Result(s): During the study it becomes clear that very few percentage of consumers (17%) consulted by a pharmacy employees. Most of them don't get the necessary information from the pharmacy employee about medicine. Only 29 % of consumers are clearly satisfied with the answers of a pharmacy employee and 26% fully trust them. Conclusion(s): Steps should be taken for improving the professional knowledge of pharmacists about medicines and pharmaceutical care, which, in turn, can restore consumer trust in them, will help avoid self-medication errors by providing advice on medicines in response to public health crises such as COVID-19. There is a need to develop pharmaceutical care algorithms for minor ailments, national emergency drug formularies for COVID-19.Copyright © 2023

An optimisation approach for determining the efficiency of vital medical devices in intensive care units with COVID-19 patients using Apriori algorithm

Improving the process of strategic management in hospitals preparation and equipping the intensive care units (ICUs) and the availability of medical devices plays an important role for knowing consumer behaviour and need. This cross-sectional study was performed in the ICU of Farhikhtegan Hospital, Tehran, Iran for a period of six months. During these months, ten medical devices have been used 5,497 times. These devices include: ventilator, oxygen cylinder, infusion pump, electrocardiography machine, vital signs monitor, oxygen flowmeter, wavy mattress, ultrasound sonography machine, ultrasound echocardiography machine, and dialysis machine. The Apriori algorithm showed that four devices: ventilator, oxygen cylinder, vital signs monitoring device, oxygen flowmeter are the most used ones by patients. These devices are positively correlated with each other and their confidence is over 80% and their support is 73%. For validating the results, we have used equivalence class clustering and bottom-up lattice traversal (ECLAT) algorithm in our dataset.

Deep learning-based COVID-19 triage tool: An observational study on an X-ray dataset

Background: Easy availability, low cost, and low radiation exposure make chest radiography an ideal modality for coronavirus disease 2019 (COVID-19) detection. Objective(s): In this study, we propose the use of an artificial intelligence (AI) algorithm to automatically detect abnormalities associated with COVID-19 on chest radiographs. We aimed to evaluate the performance of the algorithm against the interpretation of radiologists to assess its utility as a COVID-19 triage tool. Material(s) and Method(s): The study was conducted in collaboration with Kaushalya Medical Trust Foundation Hospital, Thane, Maharashtra, between July and August 2020. We used a collection of public and private datasets to train our AI models. Specificity and sensitivity measures were used to assess the performance of the AI algorithm by comparing AI and radiology predictions using the result of the reverse transcriptase-polymerase chain reaction as reference. We also compared the existing open-source AI algorithms with our method using our private dataset to ascertain the reliability of our algorithm. Result(s): We evaluated 611 scans for semantic and non-semantic features. Our algorithm showed a sensitivity of 77.7% and a specificity of 75.4%. Our AI algorithm performed better than the radiologists who showed a sensitivity of 75.9% and specificity of 75.4%. The open-source model on the same dataset showed a large disparity in performance measures with a specificity of 46.5% and sensitivity of 91.8%, thus confirming the reliability of our approach. Conclusion(s): Our AI algorithm can aid radiologists in confirming the findings of COVID-19 pneumonia on chest radiography and identifying additional abnormalities and can be used as an assistive and complementary first-line COVID-19 triage tool.Copyright © Cancer Research, Statistics, and Treatment.