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 and immunity

Coronavirus disease-19 (COVID-19), caused by a β-coronavirus and its genomic variants, is associated with substantial morbidities and mortalities globally. The COVID-19 virus enters host cells upon binding to the angiotensin converting enzyme two receptors. Patients afflicted with COVID-19 may be asymptomatic or present with critical symptoms possibly due to diverse lifestyles, immune responses, aging, and underlying medical conditions. Geriatric populations, especially men in comparison to women, with immunocompromized conditions, are the most vulnerable to severe COVID-19-associated infections, complications, and mortalities. Notably, whereas immunomodulation, involving nutritional consumption, is essential to protecting an individual from COVID-19, immunosuppression is detrimental to the host with this hostile disease. As such, immune health is inversely correlated to COVID-19 severity and resulting consequences. Advances in genomic and proteomic technologies have helped us to understand the molecular events underlying symptomatology, transmission, and pathogenesis of COVID-19 and its genomic variants. Accordingly, there has been development of a variety of therapeutic interventions, ranging from mask wearing to vaccination to medication. Regardless of various measures, a strengthened immune system can be considered as a high priority of preventive medicine for combating this highly contagious disease. This chapter provides an overview of pathogenesis, effects of comorbidities on COVID-19 and their correlation to immunity, and prospective therapeutic strategies for the prevention and treatment of COVID-19. © 2023 Elsevier Inc. All rights reserved.

Investigating the Reasons for Receiving the Second Booster Dose of the COVID-19 Vaccine in Adults and in People with Chronic Medical Conditions in Southern Italy.

This cross-sectional survey explored the attitudes and the reasons, as well their associated factors, for receiving the second booster dose of the COVID-19 vaccine among a sample of all old adults and of people with chronic medical conditions attending two randomly selected immunization centers in Naples (Italy). A total of 438 questionnaires were collected. The majority were male (55.1%) and the median age was 71 years. A higher perception of the vaccine's utility, measured with a 10-point Likert type scale, has been observed among males, individuals with a higher perception that COVID-19 is a severe illness, with a higher self-awareness of being at risk of infection, and with a higher trust in the information received. The most reported reasons for receiving the second booster dose included protection of themselves and of their family members from getting COVID-19, fear of acquiring the disease, and having a physician's recommendation. Younger participants, married/cohabitant, and with a higher perception that COVID-19 is a severe illness were more likely to have indicated protecting themselves and their family members as reason for receiving the booster dose. Respondents with a chronic medical condition, with a higher perception that COVID-19 is a severe illness, with a lower trust in the information received, and informed by physicians were more likely to have received the vaccine because they perceived of being at risk of getting a severe form of the SARS-CoV-2 infection. Physicians should play a pivotal role in stressing the importance of the second booster dose and in helping individuals to make decisions.

Development and external validation of prediction models for critical outcomes of unvaccinated COVID-19 patients based on demographics, medical conditions and dental status.

Background: Multiple prediction models were developed for critical outcomes of COVID-19. However, prediction models using predictors which can be easily obtained in clinical practice and on dental status are scarce. Aim: The study aimed to develop and externally validate prediction models for critical outcomes of COVID-19 for unvaccinated adult patients in hospital settings based on demographics, medical conditions, and dental status. Methods: A total of 285 and 352 patients from two hospitals in the Netherlands were retrospectively included as derivation and validation cohorts. Demographics, medical conditions, and dental status were considered potential predictors. The critical outcomes (death and ICU admission) were considered endpoints. Logistic regression analyses were used to develop two models: for death alone and for critical outcomes. The performance and clinical values of the models were determined in both cohorts. Results: Age, number of teeth, chronic kidney disease, hypertension, diabetes, and chronic obstructive pulmonary diseases were the significant independent predictors. The models showed good to excellent calibration with observed: expected (O:E) ratios of 0.98 (95%CI: 0.76 to 1.25) and 1.00 (95%CI: 0.80 to 1.24), and discrimination with shrunken area under the curve (AUC) values of 0.85 and 0.79, based on the derivation cohort. In the validation cohort, the models showed good to excellent discrimination with AUC values of 0.85 (95%CI: 0.80 to 0.90) and 0.78 (95%CI: 0.73 to 0.83), but an overestimation in calibration with O:E ratios of 0.65 (95%CI: 0.49 to 0.85) and 0.67 (95%CI: 0.52 to 0.84). Conclusion: The performance of the models was acceptable in both derivation and validation cohorts. Number of teeth was an additive important predictor of critical outcomes of COVID-19. It is an easy-to-apply tool in hospitals for risk stratification of COVID-19 prognosis.

Health Anxiety, Information Anxiety, and Internet Self-Efficacy on Cyberchondria among Filipino Young Professionals during the COVID-19 Pandemic

The mobility restrictions due to COVID-19 lockdown impositions have forced people to stay at home in lieu of face-to-face activities. In effect, it has increased people's exposure to the Internet and its perils, brought by excessive information from different media that may lead to the development of health-related anxiety. This phenomenon is known as cyberchondria, where people may have experienced extreme anxiety about their physical health because of repeated internet searches concerning their medical conditions. This paper investigates the possible relationship between health anxiety, information anxiety, and computer self-efficacy toward cyberchondria. Data from a cross-sectional method using online surveys among fresh graduates aged 21-24 in several Philippine higher education institutions were analyzed. The results of the structural model test reveal that both health anxiety and information anxiety may contribute to cyberchondria. The study discusses the implication of the results and offers fruitful research directions for further studies. © ICCE 2022.All rights reserved.

Why Xi Jinping Reversed His Zero-Covid Policy in China

The rare display of public anger, with some protesters directly criticizing Mr. Xi and the Communist Party, alarmed Mr. Xi and his inner circle, the officials and advisers said. Keywords: leder;wsjworld;photo-news;Political/General News;Respiratory Tract Diseases;Global/World Issues;Health;Medical Conditions;Outbreaks/Epidemics;Politics/International Relations EN leder wsjworld photo-news Political/General News Respiratory Tract Diseases Global/World Issues Health Medical Conditions Outbreaks/Epidemics Politics/International Relations N.PAG N.PAG 1 01/11/23 20230105 NES 230105 A wave of protests coupled with urgent pleas from many corners of the government finally prodded the leader to scrap the strict lockdown system he had touted throughout the pandemic BEIJING - By the end of an otherwise triumphant Communist Party Congress for Xi Jinping in October, it was growing harder for China's leader to argue that his zero-Covid policy was working. [Extracted from the article] Copyright of Wall Street Journal (Online) is the property of Dow Jones & Company Inc 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.)

How did the dietary habits of patients with chronic medical conditions change during COVID-19?

CONTEXT: Previous studies have examined the changes in the dietary habits of general populations during the COVID-19 pandemic but have not focused on specific populations such as those with chronic medical conditions (CMCs). Prior to major vaccination efforts, 96.1% of deaths were attributed to patients with preexisting CMCs, thus it is important to examine how this population has endured changes. OBJECTIVES: The purpose of this study was to identify differences in dietary habits, lifestyle habits, and food attitudes between those with CMCs compared to the populations without chronic medical conditions (non-CMCs) since the beginning of the COVID-19 pandemic. METHODS: An online cross-sectional study was conducted from May 2021 to July 2021. Participants (n=299) responded to a 58-item questionnaire regarding demographics (n=9), health information (n=8), lifestyle habits (n=7), dietary habits (n=28), and food attitudes (n=6). Frequency counts and percentages were tabulated, and t-test sampling and ANOVA testing were conducted to examine the associations utilizing SPSS V28 at a statistical significance level of p<0.05. RESULTS: When compared to non-CMC participants, with CMCs had a less frequent change in their diet and had better food attitudes when it came to consumption habits. Non-CMC and CMC participants had no statistically significant differences in overall dietary habits; however, an examination of specific food items reviews significant findings. Compared to non-CMC participants, those with CMCs reported significantly decreased consumption of energy-dense food such as French fries, white pasta, sweets, and salty snacks, with notable exceptions in increased consumption of energy-dense foods, starchy veggies, and vegetable/tomato juice. CONCLUSIONS: These findings indicate that participants with CMCs indicated that fewer changes occurred in participants with a CMC; however, when these participants made changes, they were beneficial to their consumption habits. Future studies should aim to develop interventions for the demographics with poor dietary habits so that those that are most vulnerable may have their needs met.

Internet of Wearable Medical Things for COVID-19 Diagnostics

Wearable devices have played a key role in the medical industry, especially since the COVID-19 pandemic spread. The need for a self-monitoring system increased since the spread of the virus. With the development of semiconductor technology and the increased research and development in medical wearable devices, wearable devices have been able to detect the medical condition of patients. This paper presents a biomedical wearable device to monitor the vital signs of patients. The device can be used to detect the patient COVID-19 infection. Data were extracted using different sensors and other components, and results were displayed on a mobile application that showed the health status of the patient. A PCB (Printed Circuit Board) design was made for the purpose of making the system a wearable device. The system power consumption ranged from 5-37.5mW. © 2022 IEEE.

Relations between indoor rehabilitation and basic health services in a developing country.

Background: and Introduction: Physical rehabilitation is vital for patients to regain maximum function. Approximately 80% of people with a disability live in developing countries, where they face multiple challenges in rehabilitation. The goal of the study was to conduct an analysis of indoor rehabilitation programs based on the demographics and medical conditions of the admitted patients and to relate to the available basic health and rehabilitation facilities. Methods: This was a mixed method study conducted in an inpatient rehabilitation ward of a tertiary level academic university hospital in a developing country. All admitted patients who stayed for a period of minimum two weeks were included in the study. Demographic and clinical data were obtained by means of a retrospective medical record review utilizing a standardized data extraction form. The study was further strengthened by an online literature search for the available documents for analysis, relation, and discussion. Results: Among the 1,309 admitted patients was male- female ratio was 10:7, with the majority (31.4%) cases falling between the ages of 46 and 60yrs. Rehabilitation outpatient department was the principal mode of admission (78%), and musculoskeletal and neurological conditions represented the maximum number (79.8%). Majority of patients (60.8%) were discharged home on completion of the rehabilitation program with a large number of patients who were absconded. Poor health budget allocation and lack of prioritization of the rehabilitation sector face multiple challenges, including the rehabilitation team functioning resources, space crisis for expansion which was further impacted by the COVID-19 pandemic. Conclusions: The country's current health-related rehabilitation process and socio-demographic variables have a negative relationship. There was a large number of missing data in the medical records and many patients were lost prematurely from the indoor rehabilitation program. Musculoskeletal disorders were common, and the majority of patients were discharged home once the program was completed.

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. Copyright © 2022, Anka Publishers. All rights reserved.

Nursing personnel management during COVID-19 pandemic: An exemption trend in view of health reasons.

Background: In the COVID-19 pandemic, the healthcare system faced unprecedented challenges with increased number of patients and limited resources. Managing nursing resource was a major challenge for hospital administration. They had to be on the frontline, but their safety was of paramount importance. Aim: This study aims to analyze the measures taken for the management and effective engagement of nursing personnel for deployment in the COVID area of the hospital and the exemption trend based on their health status. Methodology: A retrospective cross-sectional descriptive study was carried out to analyze the requests of nursing staff received for exemption of duty in COVID patient care areas. These requests were categorized and examined by the medical board constituted for this purpose. Microsoft Excel was used to interpret the results. Results: The study evaluated the health reasons of nursing officers on the basis of which exemption was given for deployment of nursing officers in COVID areas. They were mostly medical reasons (91.1%) and few personal reasons (8.77%). The majority suffered from diseases affecting two or more than two specialties. Out of 376 applications, 223 were exempted, 81 were not exempted, 13 were given short-term exemption, and 26 were shifted to administrative assignments. Thirty-three staff members were referred to an appropriate forum.

Resilience of people with chronic medical conditions during the COVID-19 pandemic: a 1-year longitudinal prospective survey.

BACKGROUNDS: Individuals with chronic medical conditions are considered highly exposed to COVID-19 pandemic stress, but emerging evidence is demonstrating that resilience is common even among them. We aimed at identifying sustained resilient outcomes and their predictors in chronically ill people during the first year of the pandemic. METHODS: This international 4-wave 1-year longitudinal online survey included items on socio-demographic characteristics, economic and living situation, lifestyle and habits, pandemic-related issues, and history of mental disorders. Adherence to and approval of imposed restrictions, trust in governments and in scientific community during the pandemic were also investigated. The following tools were administered: the Patient Health Questionnaire, the Generalized Anxiety Disorder scale, the PTSD Checklist DSM-5, the Oslo Social Support Scale, the Padua Inventory, and the Portrait Values Questionnaire. RESULTS: One thousand fifty-two individuals reporting a chronic condition out of 8011 total participants from 13 countries were included in the study, and 965 had data available for the final model. The estimated probability of being "sustained-resilient" was 34%. Older male individuals, participants employed before and during the pandemic or with perceived social support were more likely to belong to the sustained-resilience group. Loneliness, a previous mental disorder, high hedonism, fear of COVID-19 contamination, concern for the health of loved ones, and non-approving pandemic restrictions were predictors of not-resilient outcomes in our sample. CONCLUSIONS: We found similarities and differences from established predictors of resilience and identified some new ones specific to pandemics. Further investigation is warranted and could inform the design of resilience-building interventions in people with chronic diseases.

Differences in Oral Lesions Associated with Tobacco Smoking, E-Cigarette Use and COVID-19 Infection among Adolescents and Young People in Nigeria.

COVID-19 infection is associated with oral lesions which may be exacerbated by tobacco smoking or e-cigarette use. This study assessed the oral lesions associated with the use of e-cigarettes, tobacco smoking, and COVID-19 among adolescents and young people in Nigeria. A national survey recruited 11-23-year-old participants from the 36 States of Nigeria and the Federal Capital Territory, Abuja. Data were collected using Survey Monkey®. Binary logistic regression analysis was conducted. Statistical significance was set at p-value less than 0.05. There were 2870 participants, of which 386 (13.4%) were tobacco smokers, 167 (5.8%) e-cigarette users, and 401 (14.0%) were both e-cigarette and tobacco users; and 344 (12.0%) had ever tested positive to COVID-19. Adolescents and young people who smoked tobacco had more than twice the odds of reporting gingival inflammation, oral ulcers, dry mouth, and changes in taste than those who did not smoke. Those who used e-cigarettes had 1.5 times higher odds of reporting oral lesions. Respondents who had COVID-19 infection had higher odds of reporting gingival inflammation and lower odds of reporting dry mouth than those who did not have COVID-19 infection. These findings were significant, and may help clinicians to screen for tobacco use and COVID-19 among adolescents and young people in Nigeria.

Design COVID-19 Ontology: A Healthcare and Safety Perspective

The COVID-19 pandemic has flooded a vast amount of information into the world. To help control this situation, good utilization of the overflow in data is required. However, data come in different forms, posing numerous challenges in subsequent processing. Therefore, a uniform knowledge representation of COVID-19 information is needed, and ontology can play a role. The ontology will model patient healthcare-related data, ranging from symptoms to side effects and medical conditions, and the necessary precautions, especially for healthcare workers, to obtain protection from the COVID-19 virus. We followed Sánchez’s methodology to build the vocabularies, which include current ontology concepts, W3C standards RDF, OWL and SWRL. This work shows promising results that can be applied by different organizations. © 2022, The Author(s).

Remote Health Monitoring and Doctor on Call System

People's lives can be lost if they do not receive timely medical treatment;therefore, prompt medical care is vital. Furthermore, due to a lack of constant vital monitoring, early symptoms of major medical conditions, such as an irregular heartbeat or abnormal ECG output, are occasionally ignored. As a result, remote monitoring of the elderly and disabled is important during the COVID 19 outbreak. To solve these issues, a "Remote Health Monitoring and Doctor on Call System"has been developed. Health data exchange between healthcare providers and family members is becoming more prevalent these days. It ensures that patients' health results are safer and better. Sharing health-care data is also essential for lowering health-care expenses. It is feasible to remotely monitor a crippled or elderly patient's health. In addition to specialists, guardians can obtain a comprehensive picture of the patient's medical history. To address the issue of insufficient medical care and assistance for elderly and disabled patients, a unique and comprehensive Remote Health Monitoring and Doctor on Call System that can monitor the patient's vital signs such as heart rate, body temperature, and ECG output of the heart;monitor the patient's environment;display and store all of this information via a cloud server using ThingSpeak and Ubidots IoT platform;display these critical statistics with a mobile application for Android/iOS and in the event of an emergency or if vital signs are abnormal, contact the nearest hospital was developed in this paper. © 2022 IEEE.

Suicidal behaviours among ugandan university students: A cross-sectional study

Background: Suicide remains the leading cause of death among university students often resulting from multiple physical and psychological challenges. Moreover, suicidal behaviours among students appear to have increased due to the COVID-19 pandemic according to some studies. Objective: To explore the prevalence and associated factors for suicidal ideation, suicide plans, and suicide attempts among university students in Uganda. Methods: Cross-sectional study data were collected from May to September 2021 from 540 undergraduate university students in south-western Uganda (363 males, mean age 23.3 years). Questions from the General Health Questionnaire (GHQ-28) were used to assess suicidal ideation, while other bespoke questions were used to assess suicide plans and attempts. The survey also investigated the suicide attempt/plan method, location of the suicidal activity, and reason for not enacting the suicide plan. Three independent regression analyses were used to determine the factors associated with different forms of suicidal behaviours. Results: The prevalence of past-year suicidal behaviours was 31.85% for suicidal ideation, 8.15% for suicide plans, and 6.11% for suicide attempts. Having a chronic physical medical condition increased the likelihood of having all forms of suicidal behaviours. Suicidal ideation was associated with having difficulty paying university tuition fees. However, being in the fifth year of university education, and feeling satisfied with current academic grades reduced the likelihood of suicidal ideation. Individuals feeling satisfied with academic performance appeared to be a protective factor against having suicide plans. Suicide attempts were associated with having a history of sexual abuse and having difficulty paying university tuition fees. The most common method used for attempted suicide was a drug overdose, and the most common location for attempted suicide was their homes. Conclusion: University students have prevalent suicide behaviours especially among students with a chronic physical medical condition, a history of sexual abuse, and problems paying university tuition fees. Based on the present study, for students at risk, universities should provide appropriate interventions such as life skills education and suicide prevention techniques. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Impact of the COVID-19 Pandemic on Cardiology Emergency Department Visits.

In 2020, decreased emergency department (ED) visits and hospitalization rates during the COVID-19 outbreak were reported. There is no data about cardiovascular emergencies and mortality for the whole COVID-19 year.This study aimed to compare the rates of cardiology ED visits, hospital admissions, and intrahospital mortality between the pre-COVID-19 and COVID-19 years in a single high-volume center.The retrospective observational cross-sectional study analyzed data on the number of ED visits, hospital admissions by different cardiovascular diagnoses, and outcomes.A total of 11744 patients visited the cardiology ED in the pre-COVID-19 year compared with 9145 in the COVID-19 year, indicating an overall decrease of 22.1% (P = 0.02) (IR 78.76 versus 61.33; incidence rate ratios (IRR) 1.28, P = 0.00), with an observed decrease of 25.5% in the number of hospitalizations (33.1% versus 31.6%, P = 0.02). A marked decrease in hospitalizations for cardiovascular emergencies was observed for hypertensive heart disease (-72.8%, P < 0.0001), acute coronary syndrome (-17.8%, P < 0.0001), myocardial and pericardial diseases and endocarditis (-61.2%, P = 0.00), and valvular heart disease (-70.8%, P < 0.0001). In the COVID-19 year, patients had increased need for mechanical ventilatory support (7% versus 6.3%, P = 0.03) with no overall difference in intrahospital mortality (IR 2.71 versus 2.78, IRR 0.98, 95% CI 0.82-1.16, P = 0.39).Decreased ED visits and hospitalizations not just in outbreaks but through the whole COVID-19 year highlight the risk of continuous delay of needed care for emergency life-threatening cardiovascular diseases. Urgent comprehensive strategies that will address patient- and system-related factors to decrease morbidity and mortality and prevent collateral damage of the pandemic are needed.

Modeling of Critically Ill Patient Pathways to Support Intensive Care Delivery

The COVID-19 pandemic has exposed long standing deficiencies in critical care knowledge and practice in hospitals worldwide. New methods and strategies to facilitate timely and accurate interventions are needed. A virtual counterpart (digital twin) to critically ill patients would allow bedside providers to visualize how the organ systems interact to cause a clinical effect, offering them the opportunity to evaluate the effect of a specific intervention on a virtual patient before exposing an actual patient to potential harm. This work aims at developing a digital simulation that models the clinical pathway of critically ill patients. Using the mixed-methods approach with the support of multiprofessional clinical experts, we first identify the causal and associative relationships between organ systems, medical conditions, clinical markers, and interventions. We record these relationships as structured expert rules, depict them in a directed acyclic graph (DAG) format, and store them in a graph database (Neo4j). These structured expert rules are subsequently utilized to drive a simulation application that enables users to simulate the state trajectory of critically ill patients over a given simulated time period to test the impact of different interventions on patient outcomes. This simulation model will be the engine driving a future digital twin prototype, which will be used as an educational tool for medical students, and as a bedside decision support tool to enable clinicians to make faster and more informed treatment decisions. IEEE

Smart Stethoscope: Intelligent Respiratory Disease Prediction System

Diagnosing and treating lung diseases can be challenging since the signs and symptoms of a wide range of medical conditions can indicate interstitial lung diseases. Respiratory diseases impose an immense worldwide health burden. It is even more deadly when considering COVID-19 in present times. Auscultation is the most common and primary method of respiratory disease diagnosis. It is known to be non-expensive, non-invasive, safe, and takes less time for diagnosis. However, diagnosis accuracy using auscultation is subjective to the experience and knowledge of the physician, and it requires extensive training. This study proposes a solution developed for respiratory disease diagnosis. 'smart Stethoscope' is an intelligent platform for providing assistance in respiratory disease diagnosis and training of novice physicians, which is powered by state-of-the-art artificial intelligence. This system performs 3 main functions(modes). These 3 modes are a unique aspect of this study. The real-time prediction mode provides real-time respiratory diagnosis predictions for lung sounds collected via auscultation. The offline training mode is for trainee doctors and medical students. Finally, the expert mode is used to continuously improve the system's prediction performance by getting validations and evaluations from pulmonologists. The smart stethoscope's respiratory disease diagnosis prediction model is developed by combining a state-of-the-art neural network with an ensembling convolutional recurrent neural network. The proposed convolutional Bi-directional Long Short-Term Memory (C- Bi LSTM) model achieved an accuracy of 98% on 6 class classification of breathing cycles for ICBHF17 scientific challenge respiratory sound database. The novelty of the project lies on the whole platform which provides different functionalities for a diverse hierarchy of medical professionals which supported by a state-of-the-art prediction model based on Deep Learning. © 2022 IEEE.