ABSTRACT
Due to COVID-19, the attempt to access medical facilities has increased. But, physical meetings are the biggest reason for the global pandemic. The healthcare industry is in a dire state of despair because of the spread of COVID, constrained health workers, and infrastructure. Healthcare accommodations are costlier than ever due to various reasons such as the global population getting busier and the rise in the number of chronic diseases. Nowadays, there is a high demand for healthcare support due to the increase in the aging population, the COVID-19 crisis, its postdisease difficulties, and other chronic diseases. Treating all the patients at hospitals is not practical nowadays due to limited healthcare facilities. The patients' care is all dependent on visits to the hospital physically, and to some extent, calls, text messages, and emails. This way it was not possible for the doctors to monitor the health of the patients continuously and assist them whenever needed. Recently, the Internet of Things (IoT) and various machine learning approaches have been widely explored in this sector. The IoT-based healthcare methods provide quality and efficiency during treatment. The IoT along with machine learning techniques can transform the healthcare industry by making it more accessible and less costly and make healthcare more facile by equipping the users with pocket amicable medical facilities. With IoT-enabled devices, remote monitoring can enable patients to better connect with doctors, thereby minimizing physical visits. It can equip doctors to easily keep track of their patient's health by utilizing wearables like fitness bands that can monitor various healthcare parameters. All these systems mostly use machine learning or deep learning approaches. This way, they will know whether to offer any immediate medical care or to make any changes to the current treatment. This chapter gives a detailed discussion on IoT and machine learning in the healthcare sector and the security concerns in this domain along with the major research challenges. © 2023 Walter de Gruyter GmbH, Berlin/Boston.
ABSTRACT
Due to the coronavirus disease (COVID-19) pandemic, most of the public work is carrying out online. Universities all around the globe moved to online education, job interviews are mainly conducting online, many first-level health consultations are happening online, and companies hold periodic meetings entirely online. Google Meet, Microsoft Team, and other online meeting software applications are widely accessible on the market. In this work, we are addressing a topic that has a lot of practical applications. In this paper, we present a method that takes a recorded video as an input and generates a written and/or audio summary of the same as an output. The suggested method can also be used to generate lecture notes from lecture videos, meeting minutes, subtitles, or storyline production from entertainment videos, among several other things. The suggested system takes the video's audio track, which is then transformed to text. In addition, we created the text summary utilising text summarising algorithms. The system's users have the option of using the text summary or creating an audio output that matches the text summary. The proposed method is implemented in Python, and the proposed scheme is evaluated using short videos acquired from YouTube. Since there is no benchmark measure for evaluating the efficiency and there is no specific dataset available for the relevant study, the proposed method is manually validated on the downloaded video set. © 2021 IEEE.
ABSTRACT
The whole world is suffering from COVID-19 pandemic. This disease has halted life and has a negative impact on physical and mental health of all individuals. Objectives: To identify impact of BMI on Covid-19 clinical features and its management in terms of relationship among patients at government hospitals, Pakistan. Study Design: Cross-sectional study. Methodology: This study enrolled 206 patients having both genders and was carried at Life Diabetes Centre, Gujrat and CMH Kharian Medical College (CKMC), over a period of 3 months, Kharian-Pakistan following ethical review committee's (ERC) approval. Statistical analysis: Data was analyzed by SPSS software, version 17. Parameters like age, gender and treatment taken were presented as frequency. Chi square was applied to see the correlation with p-value <0.05 as significant. Results: Total 206 patients were randomly selected, 89 male and 117 females. Among 206, patients (n=133) showed symptoms while rest of the patients (73) remained asymptomatic. There was no association of BMI with COVID-19 symptoms having P-value greater than 0.05. There was an association of BMI with gender as P-value (0.000*). There was an association of BMI with age having P-value (0.000*). Conclusion: From present study, we concluded that there was a correlation between BMI and individuals with higher BMI as they developed more serious symptoms and required active management strategies in comparison to individuals who were either underweight or normal weight.
ABSTRACT
Background: The lockdown due to Covid-19 has impacted certain aspects of cognition among medical students. Aim: To explore the impact of the Covid-19 lockdown on metamemory among medical students studying in a private medical college in Pakistan. Study design: Experimental study. Methodology: This study with enrolled students (n=233) was carried out after ethical review committee's (ERC) approval at CMH Kharian Medical College (CKMC), Physiology Department, Kharian-Pakistan. Both male and female medical students were enrolled. In phase 1, the students reported to the Physiology laboratory where age and gender were recorded. Metamemory was measured using the Multi-factorial Memory Questionnaire (MMQ)Satisfaction scale using the Baycrest Centre protocols for its administration and scoring. Data was analyzed by SPSS software, version 21. MMQ-Satisfaction score was presented as mean +/- SD. Statistical significance was taken at p value <0.05. Results: Among males mean +/- SD for Pre Covid-19 MMQ was 43.24 +/- 9.58 while mean +/- SD for Post Covid-19 MMQ males was 55.32 +/- 6.01. Significant difference was seen between Pre & Post Covid-19 MMQ scores with p-value of <0.000. Conclusion: We concluded that significant difference was seen between Pre & Post Covid-19 MMQ scores with p-value of <0.000. However, means among all age groups were significantly equal with the others in Pre & Post COVID-19 MMQ. Similarly, means were significantly equal among both genders for Pre & Post COVID19 MMQ.
ABSTRACT
Background: The whole world is facing one of the biggest health related disaster (COVID-19) of the century. Aim: To identify age and gender-based differences in Covid-19 clinical features and its management among patients at government hospitals, Pakistan. Study design: Cross-sectional study. Methodology: This study with enrolled subjects (n=206) was carried out after ethical review committee's (ERC) approval at Life Diabetes Centre, Gujrat and CMH Kharian Medical College (CKMC), over a period of 3 months, Kharian-Pakistan. Both male and female medical subjects were enrolled. Statistical analysis: Data was analyzed by SPSS software, version 17. Parameters like age, gender and treatment taken were presented as frequency. Chi square was applied to see the correlation with p-value <0.05 as significant. Results: Total 206 patients were randomly selected, 89 male and 117 females. Among 206, patients (n=133) showed symptoms while rest of the patients (73) remained asymptomatic. There was no association of age and gender with COVID-19 symptoms having P-value greater than 0.05. There was a significant association between treatments given was significantly related with age having P-value (0.006*). Conclusion: We concluded that there was no strong association between age and gender-based differences in Covid-19 clinical features;this could be due to small sample size.
ABSTRACT
The whole world is facing one of the biggest health related disasters of the century. As a novel disease, Covid-19 has so many parameters yet to explore. Objectives: To explore any correlation between atopy and Covid-19 among residents of Gujrat and Kharian, Punjab, Pakistan. Study Design: Cross-sectional study. Methodology: This study with enrolled subjects (n=206) was carried out after ethical review committee’s (ERC) approval at Life Diabetes Centre, Gujrat and CMH Kharian Medical College (CKMC), over a period of 3 months, Kharian-Pakistan. Both male and female medical subjects were enrolled. Statistical analysis: Data was analyzed by SPSS software, version 17. Parameters like gender, allergy and treatment taken were presented as frequency and percentage. Chi square was applied to see the correlation with p-value <0.05 as significant. Results: Total 206 patients were randomly selected, 89 male and 117 females. Among 206, only 13 patients had allergy from different allergens. Only 2 patients required hospitalization and injectable treatment. Conclusion: We concluded that there is strong affiliation between atopy and Covid-19 presentations.
ABSTRACT
The Covid-19 pandemic has wreaked havoc throughout the world, with 150 million cases to date and over 3 million lives claimed worldwide. Objectives: To explore the impact of the Covid-19 lockdown on psychological health parameters i.e. depression, anxiety and stress as well as on body mass index among medical students studying in a private medical college in Pakistan. Study Design: Experimental study. Methodology: This study with enrolled students (n=233) was carried out after ethical review committee’s (ERC) approval at CMH Kharian Medical College (CKMC), Physiology Department, Kharian-Pakistan. Both male and female medical students were enrolled. In phase 1, the students reported to the Physiology laboratory where age and gender were recorded. The pre-lockdown readings of BMI and DASS-21(Depression, Anxiety, Stress) scale were taken. In phase-2, the post-lockdown readings of BMI and DASS-21 scale were taken once the students returned to campus. Statistical analysis: Data was analyzed by SPSS software, version 21. BMI and DASS-21 score were presented as mean + SD. Statistical significance was taken at p value <0.05. Results: In present study, results showed that there was a decrease in level of depression post-Covid-19-lockdown among enrolled subjects with significant p-values (0.019*) in the pre and post covid-19-lockdown comparison. Conclusion: We concluded that significant difference was seen between Pre & Post Covid-19-lockdown depression with p-value of <0.019. However, insignificant difference was seen between Pre & Post Covid-19-lockdown anxiety and stress with p-value of >0.05. Key Words: Covid-19 lockdown, Medical students, Depression, BMI and Anxiety.
ABSTRACT
The world has now facing a health crisis due to outbreak of novel coronavirus 2019 (COVID-19). The numbers of infection and death have been rapidly increasing which result in a serious threat to the social and economic crisis. India as the second most populous nation of the world has also running with a serious health crisis, where more than 8,300,500 people have been infected and 123,500 deaths due to this deadly pandemic. Therefore, it is urgent to highlight the spatial vulnerability to identify the area under risk. Taking India as a study area, a geospatial analysis was conducted to identify the hotspot areas of the COVID-19. In the present study, four factors naming total population, population density, foreign tourist arrivals to India and reported confirmed cases of the COVID-19 were taken as responsible factors for detecting hotspot of the novel coronavirus. The result of spatial autocorrelation showed that all four factors considered for hotspot analysis were clustered and the results were statistically significant (p value < 0.01). The result of Getis-Ord Gi* statistics revealed that the total population and reported COVID-19 cases have got high priority for considering hotspot with greater z-score (> 3 and > 0.7295 respectively). The present analysis reveals that the reported cases of COVID-19 are higher in Maharashtra, followed by Tamil Nadu, Gujarat, Delhi, Uttar Pradesh, and West Bengal. The spatial result and geospatial methodology adopted for detecting COVID-19 hotspot in the Indian subcontinent can help implement strategies both at the macro and micro level. In this regard, social distancing, avoiding social meet, staying at home, avoiding public transport, self-quarantine and isolation are suggested in hotspot zones;together with, the international support is also required in the country to work jointly for mitigating the spread of COVID-19. © 2021, Korean Spatial Information Society.