Research on Chest X-Ray Image Diagnosis of Covid-19 Based on Improved ResNet

With the outbreak of covid-19 in 2020, timely and effective diagnosis and treatment of each covid-19 patient is particularly important. This paper combines the advantages of deep learning in image recognition, takes RESNET as the basic network framework, and carries out the experiment of improving the residual structure on this basis. It is tested on the open source new coronal chest radiograph data set, and the accuracy rate is 82.3%. Through a series of experiments, the training model has the advantages of good generalization, high accuracy and fast convergence. This paper proves the feasibility of the improved residual neural network in the diagnosis of covid-19. © 2023 SPIE.

DIAGNOSIS OF MEDICAL CONDITIONS IN PATIENTS USING MACHINE LEARNING MODELS

A variety of patient care and intelligent health systems can benefit from the implementation of artificial intelligence as a tool to aid caregivers. Machine learning and deep learning are two types of AI that are increasingly being used in the medical industry. Artificial intelligence methods require a large amount of clinical data from a range of imaging modalities for correct disease diagnosis. In addition, AI has greatly enhanced the quality of hospital stays, allowing patients to be released sooner and complete their recoveries at home. This article aims to provide the information on the field of AI subset i.e., machine learning-based disease detection with information that will aid them in making better decision making. This helps the researchers to classify the medical conditions in patients with a prominent dataset.

COVID-19: Prediction, Prevalence, and the Operations of Vaccine Allocation

Problem definition: Mitigating the COVID-19 pandemic poses a series of unprecedented challenges, including predicting new cases and deaths, understanding true prevalence beyond what tests are able to detect, and allocating different vaccines across various regions. In this paper, we describe our efforts to tackle these issues and explore the impact on combating the pandemic in terms of case and death prediction, true prevalence, and fair vaccine distribution. Methodology/results: We present the methods we developed for predicting cases and deaths using a novel machine-learning-based aggregation method to create a single prediction that we call MIT-Cassandra. We further incorporate COVID-19 case prediction to determine true prevalence and incorporate this prevalence into an optimization model for efficiently and fairly managing the operations of vaccine allocation. We study the trade-offs of vaccine allocation between different regions and age groups, as well as first- and second-dose distribution of different vaccines. This also allows us to provide insights into how prevalence and exposure of the disease in different parts of the population can affect the distribution of different vaccine doses in a fair way. Managerial implications: MIT-Cassandra is currently being used by the Centers for Disease Control and Prevention and is consistently among the best-performing methods in terms of accuracy, often ranking at the top. In addition, our work has been helping decision makers by predicting how cases and true prevalence of COVID-19 will progress over the next few months in different regions and utilizing the knowledge for vaccine distribution under various operational constraints. Finally, and very importantly, our work has specifically been used as part of a collaboration with the Massachusetts Institute of Technology's (MIT's) Quest for Intelligence and as part of MIT's process to reopen the institute.

Explicable AI for surveillance and interpretation of Coronavirus using X-ray imaging

Explainable AI (XAI) is one of the disciplines being investigated, with the goal of improving the transparency of black-box systems. XAI is such a technology that could assist to alleviate the black-box system by providing new ways of understanding the core thinking process of AI systems. Conside ring the healthcare domain, doctors are still not able to explain why certain decisions or forecasts had been predicted by a particular system. As a result, it imposes limitations on how and where AI technology can be implemented. And to address this problem, a taxonomy of model interpretability is framed for conceptualizing the explainability. Also, an approach with the baseline system is created which could firstly differentiate in the Covid-19 positive and Covid-19 negative chest X-ray images and an automated explainable pipeline is designed using XAI technique. This technique shows that the model is interpretable, that is the achieved results are easy to understand and can encourage medicians and patients with transparent and reliable medical journey. This article aims to help people comprehend the necessity for Explainable AI, as well as the methodological approaches used in healthcare. © 2023 IEEE.

Telephone triage nurses and telemedicine providers

There are two main exceptions: 1 Triage questionsthatfall underthe 911 orGoto emergency department (ED) Now dispositions. Nurse awareness of PCP preferences also play a role in deciding who is appropriate to refer for TM. On weekends and holidays, call center nurses can schedule patients triaged to the video visit within 24 hours disposition with on-call PCPs who are willing to provide this service during the day.

Methods for Detecting COVID-19 Patients Using Interval-Valued T-Spherical Fuzzy Relations and Information Measures

The concepts of relations and information measures have importance whenever we deal with medical diagnosis problems. The aim of this paper is to investigate the global pandemic COVID-19 scenario using relations and information measures in an interval-valued T-spherical fuzzy (IVTSF) environment. An IVTSF set (IVTSFS) allows describing four aspects of human opinions i.e., membership, abstinence, non-membership, and refusal grade that process information in a significant way and reduce information loss. We propose similarity measures and relations in the IVTSF environment and investigate their properties. Both information measures and relations are applied in a medical diagnosis problem keeping in view the global pandemic COVID-19. How to determine the diagnosis based on symptoms of a patient using similarity measures and relations is discussed. Finally, the advantages of dealing with such problems using the IVTSF framework are demonstrated with examples.

Priority Degrees and Distance Measures of Complex Hesitant Fuzzy Sets With Application to Multi-Criteria Decision Making

The notion of a complex hesitant fuzzy set (CHFS) is one of the better tools in order to deal with complex information. Since distance plays a crucial role in order to differentiate between two things or sets, in this paper, we first develop a priority degree for the comparison between complex hesitant fuzzy elements (HFEs). Then a variety of distance measures are developed, namely, Complex hesitant normalized Hamming-Hausdorff distance (CHNHHD), Complex hesitant normalized Euclidean-Hausdorff distance (CHNEHD), Generalized complex hesitant normalized Hausdorff distance (GCHNHD), Complex hesitant hybrid normalized Hamming distance (CHHNHD), Complex hesitant hybrid normalized Euclidean distance (CHHNED), Generalized complex hesitant hybrid normalized distance (GCHHND) and their weighted forms. Moreover, the continuous form of the proposed distances is also developed. Further, the proposed distances are applied to medical diagnosis problems for their effectiveness and application. Furthermore, a multi-criteria decision making (MCDM) approach is developed based on the TOPSIS method and proposed distances. Finally, a practical example related to the effectiveness of COVID-19 tests is presented for the application and validity of the proposed method. A comparison study was also done with the method that was already in place to see how well the new method worked.

Developing and Implementing a Depression Screening and Referral Protocol Among College Students

Depression is prevalent among college students but remains underrecognized and undertreated. Evidence supports universal depression screening among college students combined with follow-up systems to ensure appropriate diagnosis and management. Screening tools may include versions of the Patient Health Questionnaire, and follow-up systems may include following up with the current provider or referring the student to a mental health specialist. The purpose of this quality improvement project was to promote the identification of college students with depression and subsequent appropriate referrals.

Delay in Cancer Screening and Diagnosis During the COVID-19 Pandemic: What Is the Cost?

[...]standard cancer screening such as breast cancer screenings dropped by 89.2% and colorectal cancer screenings dropped by 84.5% through May 2020.1 These pandemic control efforts translated into a significant decline in the number of new cancer diagnoses, resulting in a decrease of 65.2% incidence of new cancer diagnoses in April 2020.1 In evaluating specific types of cancer diagnosis, patients with a new diagnosis of melanoma dropped 67.1% in April 2020 compared with 2019 and a diagnosis of a new lung cancer which dropped 46.8% over the same time.1 This study and others have demonstrated an alarming decrease in the diagnosis of new cancers which will potentially increase the number of patients with later-stage cancers leading to decreased survival for these patients.2,3 Using National Health Service (NHS) data on cancer diagnosis and hospital administrative datasets, the investigators' modeling study evaluated estimated changes in future death rates. Across different scenarios as compared with prepandemic figures, the investigators estimated a 7.9% to 9.5% increase in deaths from breast cancer up to 5 years from diagnosis.3 In addition, a 15.5% to 16.6% increase in colorectal cancer deaths and a 4.8% to 5.3 % increase in lung cancer deaths were estimated.3 In addition to health care facilities decreasing routine screening and nonurgent surgeries to increase capacity for patients with COVID-19 complications, patients themselves have in some cases expressed concern about visiting the health care facilities to do routine cancer screenings for fear of COVID-19 exposure. Prior to the COVID-19 pandemic, the US cancer statistics had continued to improve over the last few decades including a 25% drop in cancer mortality over the past 25 years.4 However, with less cancer screening comes the potential for malignancies to be diagnosed at a later stage.

COVIDPRO-NET: a prognostic tool to detect COVID 19 patients from lung X-ray and CT images using transfer learning and Q-deformed entropy

The humankind had faced several pandemic outbreaks, and coronavirus illness (COVID-19) caused by severe, acute respiratory syndrome coronavirus 2, is designated an emergency by the World Health Organization (WHO). Recognition of COVID-19 is a challenging task. The most commonly used methods are X-ray and CT scans images to inspect COVID-19 patients. It requires specialised medical professionals to report each patient's health manually. It is found that COVID-19 shows considerable similarity to pneumonia lung disease. Thus, knowledge learned from a model to diagnose pneumonia can be translated to identify COVID-19. Transfer learning method offers a drastic performance when compared with results from conventional classification. In this study, Image pre-processing is done to alleviate intensity variations between medical images. These processed images undergo a feature extraction which is accomplished using Q-deformed entropy and deep learning extraction. The feature extraction techniques are employed to remove abnormal markers from images, noise impedance from tissues and lesions. The traits acquired are integrated to differentiate between COVID-19, pneumonia and healthy cases. The primary aim of this model is to produce an image processing tool for medical professionals. The model results to inspect how a healthy or COVID-19 individual outperforms conventional models. The maximum accuracy of the collected data set is 99.68%. [ FROM AUTHOR] Copyright of Journal of Experimental & Theoretical Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Negative Impact of the COVID-19 Pandemic on Oncology Care at an Academic Cancer Referral Center

Methods Patient Cohort Under a protocol approved by the University Hospitals Seidman Cancer Center Institutional Review Board, a search of all appointment data from patients with cancer in the in the Seidman Cancer Center was performed. For each of the 4 groups of appointment types, rates of cancellation (cancellation count divided by appointment count) were stratified by age group (0-39 years, 40-64 years, 65 years or older),10 sex (male or female), and race (White, Black, or other) on a monthly basis. Descriptive statistics were used to assess any association of cancellation rate between 2019 and 2020 for both overall data, and stratified by age group, sex, and race for each appointment type respectively, where the ÷2 test of independence was used for comparison. The trend comparison of appointment rates was also examined by trend plot both for overall data and stratified by age group, sex, and race for each appointment type respectively.

Differentiating Between COVID-19 and Tuberculosis Using Machine Learning and Natural Language Processing

Over 10 million people around the world are affected by tuberculosis (TB) every year, making it a major global health concern. With the advent of the COVID-19 pandemic, TB services in many countries have been temporarily disrupted, leading to a potential delay in the diagnosis of TB cases and many cases going under the radar. Since both diseases sometimes present similarly and generally affect the lungs, there is also a risk of misdiagnosis. This study aims to analyse the differences between COVID-19 and TB in different patients, as a first step in the creation of a TB screening tool. 180 COVID-19 and 215 TB case reports were collected from ScienceDirect. Using Natural Language Processing tools, the patient's age, gender, and symptoms were extracted from each report. Tree-based machine learning algorithms were then used to classify each case report as belonging to either disease. Overall, the cases included 252 male and 117 female patients, with 26 cases not reporting the patient's sex. The patients' ages ranged from 0 to 95 years old, with a median age of 41.5. There were 33 cases with missing age values. The most frequent symptom in the TB cases was weight loss while most COVID-19 cases listed fever as a symptom. Of all algorithms implemented, XGBoost performed best in terms of ROC AUC (86.9 %) and F1-score macro (78%). The trained model is a good starting point, which can be used by medical staff to aid in referring potential TB patients in a timely manner. This could reduce the delay in TB diagnosis as well as the TB death toll, especially in highly infected countries.

Toward a knowledge graph for medical diagnosis: issues and usage scenarios

The healthcare industry faces many challenges like demand for high-quality remote services, especially when pandemics like COVID-19 spread across a region or even all over the world. Due to these challenges, healthcare providers are adopting innovative technologies to build new systems with enhanced automation for disease detection and assistance. For instance, a system able to support medical doctors to detect potential diseases when analyzing symptoms of a patient can help to treat the patient in a quicker and more effective manner, e.g., by routing her/him to the right specialist. Diagnostic systems need a significant amount of background knowledge in the medical sector, which can be enhanced by using semantics for knowledge representation, sharing, information integration and extraction, and reasoning. To this purpose, we propose a knowledge graph for medical diagnosis leveraging existing largely used standards and ontologies and we present the main issues in aligning them. Then we describe some usage scenarios for the knowledge graph. In detail, the knowledge graph for medical diagnosis encompasses SNOMED CT, ICD-10-CM, and DOID ontology. © 2022 Elsevier Inc. All rights reserved.

Nonlinear distance measures under the framework of Pythagorean fuzzy sets with applications in problems of pattern recognition, medical diagnosis, and COVID-19 medicine selection.

Background: The concept of Pythagorean fuzzy sets (PFSs) is an utmost valuable mathematical framework, which handles the ambiguity generally arising in decision-making problems. Three parameters, namely membership degree, non-membership degree, and indeterminate (hesitancy) degree, characterize a PFS, where the sum of the square of each of the parameters equals one. PFSs have the unique ability to handle indeterminate or inconsistent information at ease, and which demonstrates its wider scope of applicability over intuitionistic fuzzy sets. Results: In the present article, we opt to define two nonlinear distances, namely generalized chordal distance and non-Archimedean chordal distance for PFSs. Most of the established measures possess linearity, and we cannot incorporate them to approximate the nonlinear nature of information as it might lead to counter-intuitive results. Moreover, the concept of non-Archimedean normed space theory plays a significant role in numerous research domains. The proficiency of our proposed measures to overcome the impediments of the existing measures is demonstrated utilizing twelve different sets of fuzzy numbers, supported by a diligent comparative analysis. Numerical examples of pattern recognition and medical diagnosis have been considered where we depict the validity and applicability of our newly constructed distances. In addition, we also demonstrate a problem of suitable medicine selection for COVID-19 so that the transmission rate of the prevailing viral pandemic could be minimized and more lives could be saved. Conclusions: Although the issues concerning the COVID-19 pandemic are very much challenging, yet it is the current need of the hour to save the human race. Furthermore, the justifiable structure of our proposed distances and also their feasible nature suggest that their applications are not only limited to some specific research domains, but decision-makers from other spheres as well shall hugely benefit from them and possibly come up with some further extensions of the ideas.

Role of CT scan thorax in nCovid19—a case-based review

BackgroundMost of the morbidity and mortality in nCovid19 is due to pneumonia which can be reduced by early diagnosis and treatment. Chest CT scan plays an important role in the early diagnosis and management of respiratory complications due to nCovid19. Clinicians should be aware about the indications for the CT scan of the thorax, timing of investigation, and limitations of CT.Main body of abstractChest CT scan is indicated in patients with moderate to severe respiratory symptoms and pretest probability of nCovid19 infection, when RT-PCR test results are negative, and in patients for whom an RT-PCR test is not performed or not readily available. When a rapid antigen test is negative and an RT-PCR test report takes time, CT can be used in seriously ill patients to decide whether it is COVID or not. For patients who are dependent on oxygen even after 2 weeks, CT may help to show the extent of lung involvement and predict long-term prognosis. CT may be done to exclude nCovid19 pneumonia. For patients with high risk for nCovid19 who require an immediate diagnosis to rule out lung involvement, CT can be done. A normal CT excludes nCovid19 pneumonia. CT scan is required in confirmed cases of nCovid19 pneumonia when complications are suspected clinically. These include pulmonary thromboembolism, pneumothorax, mediastinal/surgical emphysema, bacterial pneumonia, and unexplained deterioration with new shadows in chest X-ray. CT pulmonary angiogram is indicated when pulmonary embolism is suspected, and in other cases, plain CT should be done. In pre-operative cases where emergency surgery is required, nCovid19 disease is suspected clinically, and RT-PCR report awaited or not available, CT thorax can be done.ConclusionCT scan is useful for early diagnosis of lung involvement, detection complications, triaging of cases, risk stratification, and preoperative evaluation in select cases. CT scan should be done only when there is a definite indication so to reduce radiation hazards and to reduce health care expenditure. Normal CT excludes nCovid19 lung involvement, but the patient may have upper respiratory involvement which may progress later to involve lungs.

Towards Explainable AI on Chest X-Ray Diagnosis Using Image Segmentation and CAM Visualization

Computer vision in medical diagnosis has achieved a high level of success in diagnosing diseases with high accuracy. However, conventional classifiers that produce an image-to-label result provide insufficient information for medical professionals to judge and raise concerns over the trust and reliability of a model with results that cannot be explained. Class activation maps are a method of providing insight into a convolutional neural network's feature maps that lead to its classification but in the case of lung diseases, the region of concern is only the lungs. Therefore, the proposed model combines image segmentation models and classifiers to crop out only the lung region of a chest X-ray's class activation map to provide a visualization that improves the explainability and trust of an AI's diagnosis by focusing on a model's weights within the region of concern. The proposed U-Net model achieves 97.72% accuracy and a dice coefficient of 0.9691 on testing data from the COVID-QU-Ex Dataset which includes both diseased and healthy lungs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Autism and COVID-19

This article discusses autism and COVID-19. Autistic individuals in congregate and group settings, with co-occurring medical conditions are at higher risk for contracting COVID-19 and poor health outcomes. Wide variations in state vaccine prioritization plans exist, where high-risk disabled populations are not considered high priority. Access to routine medical visits has been disrupted during surges in cases, leading to potential delays in accessing necessary diagnoses, treatments and services. Emergency preparedness plans often overlook the needs of autistic individuals;for example, the use of the frailty scale to ration care, which unfairly disadvantages autistic individuals. Social isolation has negative effects on the well-being of autistic individuals who have lost their routine social interactions and support. The disruption to learning has been particularly concerning for children with special educational needs. The COVID-19 pandemic has highlighted areas that need urgent attention in the community. Autistic individuals, particularly those at high-risk for COVID-19-related hospitalizations and deaths, should be prioritized to receive the COVID19 vaccine. Autistic individuals must be represented in infection control and emergency preparedness planning at multiple levels: for example, within schools, health care settings, residential facilities, etc. Prolonged and unexpected disruptions to health, educational, and behavioral service deliveries during occurrences such as the COVID-19 pandemic must be met with innovative solutions to maximize individual life-course trajectories. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Promoting students' learning achievement and self-efficacy: A mobile chatbot approach for nursing training

The aims of nursing training include not only mastering skills but also fostering the competence to make decisions for problem solving. In prenatal education, cultivating nurses' knowledge and competence of vaccine administration is a crucial issue for protecting pregnant women and newborns from infection. Therefore, obstetric vaccination knowledge has become a basic and essential training program for nursing students. However, most of these training programs are given via the lecture-based teaching approach with skills practice, providing students with few opportunities to think deeply about the relevant issues owing to the lack of interaction and context. This could have a negative impact on their learning effectiveness and clinical judgment. To address this problem, a mobile chatbot-based learning approach is proposed in this study to enable students to learn and think deeply in the contexts of handling obstetric vaccine cases via interacting with the chatbot. In order to verify the effectiveness of the proposed approach, an experiment was implemented. Two classes of 36 students from a university in northern Taiwan were recruited as participants. One class was the experimental group learning with the proposed approach, while the other class was the control group learning with the conventional approach (ie, giving lectures to explain the instructional content and training cases). The results indicate that applying a mobile chatbot for learning can enhance nursing students' learning achievement and self-efficacy. In addition, based on the analysis of the interview results, students generally believed that learning through the mobile chatbot was able to promote their self-efficacy as well as their learning engagement and performance. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Initial Stage COVID-19 Detection System Based on Patients' Symptoms and Chest X-Ray Images

The accurate diagnosis of the initial stage COVID-19 is necessary for minimizing its spreading rate. The physicians most often recommend RT-PCR tests;this is invasive, time-consuming, and ineffective in reducing the spread rate of COVID-19. However, this can be minimized by using noninvasive and fast machine learning methods trained either on labeled patients' symptoms or medical images. The machine learning methods trained on labeled patients' symptoms cannot differentiate between different types of pneumonias like COVID-19, viral pneumonia, and bacterial pneumonia because of similar symptoms, i.e., cough, fever, headache, sore throat, and shortness of breath. The machine learning methods trained on labeled patients' medical images have the potential to overcome the limitation of the symptom-based method;however, these methods are incapable of detecting COVID-19 in the initial stage because the infection of COVID-19 takes 3 to 12 days to appear. This research proposes a COVID-19 detection system with the potential to detect COVID-19 in the initial stage by employing deep learning models over patients' symptoms and chest X-Ray images. The proposed system obtained average accuracy 78.88%, specificity 94%, and sensitivity 77% on a testing dataset containing 800 patients' X-Ray images and 800 patients' symptoms, better than existing COVID-19 detection methods. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Passive Suicidal Ideations in Older Adults: A Case Report

This case study presents the diagnosis and treatment of an older adult with depression and passive suicide ideation (SI). While treating depressed patients at risk for suicide, family nurse practitioners must stay grounded in patient data related to medications, ideally using the patient's psychiatric condition (ie, depression with suicidal risk) as the separate and appropriate target of clinical intervention, and discuss the risks and benefits of medications targeting both conditions with the patient. The response and ongoing management of individuals with passive SI depend on determining their risk level.