JMIRx Med

Twitter is one of the worlds biggest social media platforms for hosting abundant number of user-generated posts. It is considered as a gold mine of data. Majority of the tweets are public and thereby pullable unlike other social media platforms. In this paper we are analyzing the topics related to mental health that are recently (June, 2020) been discussed on Twitter. Also amidst the on-going pandemic, we are going to find out if covid-19 emerges as one of the factors impacting mental health. Further we are going to do an overall sentiment analysis to better understand the emotions of users. Executive SummeryNovel Corona viruss spread and its impact on various aspects of national and individuals well-being has been at the center of lot of discussions across micro-blogging sites and various social media platforms ever since it commenced in December 2019. Users are voicing their opinions on several topics related to covid-19. Social distancing as prescribed by Government and Local Administration We all are aware that the Novel Corona virus has significantly affected our physical health; however the current social distancing norms are taking a toll on the psychological well-being of individuals. The research paper presents a two-phased analysis of most recent 2000 tweets related to mental health pulled out twice over a span of one month on 28 June 2020 and 28 July2020 respectively, thereby analyzing 4000 tweets in total. The second phase analysis was conducted exactly after a gap of one month to validate the results generated by the first analysis. The intention is to analyze to what extent people have discussed about mental health in the past few months based on the information disseminated on Twitter. Data was extracted using Twitters search application programming interface (API) and Pythons tweepy library. A predefined keyword like mental health was given to find out if Covid-19 emerges as a reason for the same. Several natural language processing (NLP) techniques like tokenization, removing URL and stop words, stemming and lemmatization were used to pre-process the text data and make it ready for analysis. These collected tweets were analyzed using word frequencies of single and double words (unigram, bigram). A very unique feature of this analysis includes a network diagram that shows interconnections between the set of most common words used in to its and the connections (if any) are represented through links. Topic modeling technique in NLP visualizes the top concerns of tweeters through a word cloud. At present we have many methods to do topic modeling. In this paper we are using the Latent Dirichlet Allocation (LDA) method which is a probabilistic approach of modeling given by Prof David H.B in 2003. This model deals with distribution of topics to tweets and allocation of those topics to documents and words to topics. Finally a sentiment analysis is done using text mining techniques to analyze the sentiment of the tweets in the form of positive, negative and neutral.

BackgroundTo determine preoperative Geriatric 8 (G8) score in predicting postoperative complications for elderly head and neck cancer patients. Material and MethodsThe prospective study in elderly head and neck cancer patients who underwent surgery during 30th January 2021-25th January 2022. G8 score were collected before surgery and analysed for the association with complication outcome. ResultOf 104 patients included in this study, The mean age was 68.84 (SD =6.99 years). The Geriatric 8 (G8)score [≤] 14 which were frail group in 73 cases (70.2%) The Clavien-Dindo complications grade III-IV were 30 patients (28.8%). Among these groups, 26 patients (86.7%) was in frail groups and 4 patients (13.3%) with non frail group which is statistically significant different (p=0.019) and Odd ratio of 3.32, CI =1.01-10.87, p=0.048 ConclusionThe G8 score is a practical tool for prediction post operative complication in elderly Head and Neck Cancer surgery.

BackgroundRising fear and panic among public during COVID19 pandemic increase concern regarding anxiety cases in Kuwait. Media capture our attention during this period looking for daily virus update lead to more fear. Our purpose of this study to examine the relationship between anxiety and media exposure among Kuwaiti during COVID19 outbreak Methodcross sectional study among Kuwaiti citizen between age23-55yrs old was conducted from April,21,2020 to May,15,2020 using online survey. Total of 1230 participants involve in the current study after exclusion criteria removed. Beside demographic data and media exposure anxiety was assessed using generalized anxiety disorder scale GAD-7, multivariable regression was used to identify the correlation between anxiety and media exposure Resultthe result show that there is positive correlation between media exposure and anxiety during COVID19 outbreak in Kuwait (p<.001), furthermore it revealed that there is significant relationship between the frequency of exposure and anxiety(<.001) Conclusionfrom this study we can understand that during COVID19 pandemic exposure to media can cause anxiety therefore measures should be taken by the governments to fight misinformation and physician should pay more attention to mental health disease during this period.

BackgroundPharmacists are drug specialists in the society. The roles of pharmacists have extended beyond the typical product-oriented duties of dispensing, delivering medication and medical supplies to more patient-centered care. Patient satisfaction is a key indicator for healthcare quality and a metric to identify aspects that need improvement. ObjectiveThe aim of this study was to evaluate the public perception of pharmacists roles and satisfaction with the services they provide. MethodUsing a self-administered questionnaire, a cross-sectional descriptive study was conducted, data were analyzed using Statistical Package for Social Sciences (SPSS) version 25. Out of the 600 distributed questionnaire, 592 completed questionnaires were retrieved. ResultsMajority of the respondents were between the ages 18 and 30 years (88.5%) and had secondary school education as their highest level of educational qualification (73.6%). Higher proportion of the female respondents had a positive perception (72.4%). Also, they had higher satisfaction from the services (72.5%). Educational qualification (p=0.001), gender (p= 0.027), age (p= 0.006) and employment (p< 0.001) were significantly associated with the level of satisfaction from the services provided by pharmacists. ConclusionA good proportion of the members of the University community had a positive perception of the duties of pharmacists and were moderately satisfied with the services they provide. Steps should be taken to increase the amount and quality of time pharmacists spend with each patient.

BackgroundThe urgent need to provide fast, low-cost, affordable, and high-quality medical services is one of the requirements of todays modern world, which is characterized by a fast pace and search for the best services, especially regarding medical consultation and treatment. These demands have led to the invention of new ways that medical services can be provided to patients in their homes. MethodsA cross-sectional study, conducted in February 2022 and ended in June 2023 with a representative sample of different categories of citizens. The study population included 281 (81.9%) males and 62 (18.1%) females (SD.385, mean 1.18). The study aimed to assess the knowledge, attitudes, and practices of Saudi citizens in the Riyadh region regarding telemedicine. ResultsThe study revealed that only 30.3% of respondents were able to select the correct definition of the term telemedicine from three alternatives, which may reflect poor knowledge about telemedicine. A total of 19.5% of our participants said that they used telemedicine services only for medical consultations, while 13.7 said they used it for diagnosis and treatment. A total of 104 (88.9) out of 117 of the respondents believed that telemedicine has a positive impact on reducing costs and saving time. ConclusionThe study concluded that the main barriers that limit the use of telemedicine services are a lack of confidence in accessing telemedicine, difficulty in communicating due to the use of different languages and dialects, and the fact that services are not suitable for uneducated people.

India reported its first Covid-19 case on 30th Jan 2020 and the number of cases reported heavily escalated from March, 2020. This research paper analyses COVID -19 data initially at a global level and then drills down to the scenario obtained in India. Data is gathered from multiple data sources-several authentic government websites. The need of the hour is to accurately forecast when the numbers will reach at its peak and then diminish. It will be of huge help to public welfare professionals to plan the preventive measures to be taken keeping the economic balance of the country as well. Variables such as gender, geographical location, age etc. have been represented using Python and Data Visualization techniques. Time Series Forecasting techniques including Machine Learning models like Linear Regression, Support Vector Regression, Polynomial Regression and Deep Learning Forecasting Model like LSTM(Long short-term memory) are deployed to study the probable hike in cases and in the near future. A comparative analysis is also done to understand which model fits the best for our data. Data is considered till 30th July, 2020. The results show that a statistical model named sigmoid model is outperforming other models. Also the Sigmoid model is giving an estimate of the day on which we can expect the number of active cases to reach its peak and also when the curve will start to flatten. Strength of Sigmoid model lies in providing a count of date that no other model offers and thus it is the best model to predict Covid cases counts -this is unique feature of analysis in this paper. Certain feature engineering techniques have been used to transfer data into logarithmic scale as is affords better comparison removing any data extremities or outliers. Based on the predictions of the short-term interval, our model can be tuned to forecast long time intervals.

Withdrawal StatementThe authors have withdrawn this manuscript because they have identified a new and more promising direction for this research. As the current version no longer reflects the intended scope and findings of our ongoing study, we have decided not to share this work further. Therefore, the authors do not wish this work to be cited as a reference for the project. If you have any questions, please contact the corresponding author.

COVID-19 had a severe impact on Malaysia, as cases increased dramatically as the pandemic spread. In order to combat the pandemic, the Ministry of Health has established a number of standard operating procedures (SOP) and started operating COVID-19 Assessment Centers (CAC). This study compares the expert system created using the current patient evaluation standards to the capabilities of machine learning approaches in capturing the potential of being admitted directly or during home quarantine, based on the different clinical symptoms and age group. Boruta is a feature selection method that is employed to rank and extract significant characteristics. Treatment for imbalance has been carried out by under-sampling with K-Means and over-sampling with SMOTE. It appeared that the machine learning method using Random Forest would perform better than the expert systems. There are five performance metrics used in this study, i.e. accuracy, precision, recall, F1-score, and specificity. This study focused to maximize the true positive rate while minimize the false negative rates, it is to make sure that the patient who really need to be hospitalized will not be missed out. Therefore, recall becomes the main evaluation metrics when comparing the machine learning model and the expert system. The results shown that the recall score for machine learning approach is vastly higher then of expert systems. For age group 18-59, machine learning has 32.75% recall more than the expert system to predict if a patient requires direct admission, while for age group more than 60, the recall of machine learning is 18.11% more than expert system. In addition, to predict if a patient require admission during their home quarantine due to their health deterioration, machine learning recorded 76.72% recall more than the expert system for patient aged 18 to 59, and 70.59% difference for patient more than 60 years old. This supports the potential application of machine learning for clinical decision making for COVID-19 patients.

This work details the outbreak and factors affecting the spread of novel coronavirus (COVID-19) in the Indian state of Maharashtra, which is considered as one of the most massive and deadly pandemic outbreaks. Observational data collected between 14 March 2020 and 4 May 2020 is statistically analyzed to determine the nonlinear behavior of the epidemic. It is followed by validating predicted results with real-time data. Proposed model is further used to obtain statistical summaries in which Grubbs tests for outlier detection have justified high values of evaluation metrics. Outliers are found to be pilot elements in an outbreak under considered region. Statistically, a significant correlation has been observed between dependent and explanatory variables. Transmission pattern of this virus is very much different from the SARS-CoV-1 virus. Key findings of this work will be predominant in maintaining environment conditions at healthcare facilities to reduce transmission rates at these most vulnerable places.

The morbidity, mortality, and diminished quality of life of patients are all impacted by drug-related problems. Stroke is one of the leading causes of disability and death in India and since the stroke patients are at an increased risk of DRPs (Drug Related Problems), early identification, prevention and resolution of the same is important for improving the quality of life of stroke patients. The aim of the study was to assess the incidence of DRPs in stroke patients by using Hepler-strand classification and the acceptance rate by the multidisciplinary team. The relevant information were documented using a predefined data collection form and was analyzed for drug related problems and categorized according to Hepler-Strand classification. A total of 510 DRPs from 130 participants with an average incidence of 3.92 DRPs per patient. Drug-drug interaction was found to be 23.53% and ADRs accounted for 12.55 % of total DRPs. The study found a high acceptance rate of recommendations by healthcare professionals at 97.36%, with changes in therapy being made 68.13% of the time. Early detection and resolution of DRP by clinical pharmacist may improve the therapeutic outcomes in stroke patients. This also helps in preventing complications and unnecessary hospitalization, high cost of treatment and deaths among stroke patients.

ObjectivesThe purpose of the present study was to describe experiences of disabilities and factors affecting daily life from the perspective of adult persons with severe-to-profound hearing impairment in combination with severe vision impairment. Furthermore, the study also investigated which kind of support individuals with dual sensory loss received, and their experiences as citizens in the society. MethodsSemi-structured qualitative interviews were performed, analyzed, and categorized using content analysis. ResultsFourteen interviews were performed, with equal number of both sexes. Mean age was 70.1 years (47-81 years). Analysis of the data resulted in 22 categories, six sub-themes and two main themes. Two main themes emerged as Isolation and The Ability to control ones own daily life. Surprisingly, most of the participants did not think of their vision and hearing impairment as a combined disability. The interviews showed various kind of strategies to handle daily life. The Deafblind-team unit was reported to offer excellent health care. Companion services for persons with disabilities proved to have become more difficult to get support from and created lack of independence and control over their own lives. However, it was also obvious that the participants felt a positive outlook on life and more solution-oriented in order to adjust their everyday life to their life-situation. ConclusionsThe combination of vision and hearing impairment demonstrated isolation, and the respondents in the study have a need of support in everyday lives. At the same time, they struggle to have the ability to control their own lives.

The present work studies the reactions of citizens from United States of America (USA) during the first cases of Bubonic Plague in the territory on August 2020, during pandemic generated by Covid-19. The interest of the study is analyze the posts of users from all the states of USA following a Text Mining approach. The collection of data is performed through Twitter Application Program Interface (API) of Twitter, considering keywords: bubonic plague and black death. The results show the states with highest number of coronavirus has more publication than others and the interest about treatments, political issues and public health.

Since the first report of COVID-19 from Wuhan China, the virus has rapidly spread across the globe now presently reported in 177 countries with positive cases crossing 400 thousand and rising. In the current study, prediction is made for highly infected countries by a simple and novel method using only cumulative positive cases reported. The rate of infection per week (Rw) coefficient delineated three phases for the current COVID-19 pandemic. All the countries under study have passed Phase 1 and are currently in Phase 2 except for South Korea which is in Phase 3. Early detection with rapid and large-scale testing helps in controlling the COVID-19 pandemic. Staying in Phase 2 for longer period would lead to increase in COVID-19 positive cases.

Prioritizing dataset dependability, model performance, and interoperability is a compelling demand for improving stroke risk prediction from medical surveys using AI in healthcare. These collective efforts are required to enhance the field of stroke risk assessment and demonstrate the transformational potential of AI in healthcare. This novel study leverages the CDCs recently published 2022 BRFSS dataset to explore AI-based stroke risk prediction. Numerous substantial and notable contributions have been established from this study. To start with, the datasets dependability is improved through a unique RF-based imputation technique that overcomes the challenges of missing data. In order to identify the most promising models, six different AI models are meticulously evaluated including DT, RF, GNB, RusBoost, AdaBoost, and CNN. The study combines topperforming models such as GNB, RF, and RusBoost using fusion approaches such as soft voting, hard voting, and stacking to demonstrate the combined prediction performance. The stacking model demonstrated superior performance, achieving an F1 score of 88%. The work also employs Explainable AI (XAI) approaches to highlight the subtle contributions of important dataset features, improving model interpretability. The comprehensive approach to stroke risk prediction employed in this study enhanced dataset reliability, model performance, and interpretability, demonstrating AIs fundamental impact in healthcare.

BackgroundHospitalization for children has an impact on physical and psychological problems for children and parents. Many interventions can be done to reduce stressors for children or parents in nursing using the philosophy of atraumatic care, but there are not many articles that provide specific identification in the realm of the philosophy of atraumatic care. This study aims to determine the Evidence-Based Practice Nursing (EBPN) atraumatic care carried out by nurses in hospitals. MethodThis research uses the literature study method. The Literature Study stages include problem identification, searching data in 4 databases, namely Pubmed (370 Journals), Proquest (295 Journals), Clinical Key (751 Journals), and Science Direct (573 Journals) with a total of 1,989 journals after that through the screening method, assessment of study quality using the JBI Critical Appraisal Tool which resulted in the final results with 18 journals, after that through data extraction and data analysis methods. ResultsFrom 18 intervention journals to prevent physical and psychological stress, including the use of Virtual Reality (VR), and the use of buzzy and interactive play. Intervention impact separation with the presence of parents and involvement in care. Interventions related to the impact of the foreign environment are by modifying the nurses uniform and car orientation of the care environment. Intervention in improving treatment control with PRISM-P and Progressive Muscle Relaxation (PMR) with Guided Imagery (GI). ConclusionAll interventions that are included in the 4 philosophies of atraumatic care, show the results of the effectiveness of the intervention on each principle of atraumatic care. This can be applied to patients, especially children and the elderly according to their condition.

The length of hospital stay (LOS) and the type of discharge are important indicators of how well care is provided at a hospital. The purpose of this study is to leverage in-patient data collected at the hospital to help determine the factors that influence the length of hospital stay and type of discharge. Our research focuses on estimating if the person survived or not after they were admitted to the hospital, as well as the type of discharge. The study uses a retrospective design and examines information from hospital discharged patients medical records. Demographic information, diagnosis, treatment, and discharge status were included in the data. We have used the PEDALFAST dataset which stands for PEDiatric Validation of Variables in Trauma. A survey of patients to find out how they feel about the quality of care they received while they were in the hospital was also a part of the study dataset. The findings of this study will shed light on the ways in which various factors influence the LOS in the hospital and the type of discharge, assisting in the formulation of strategies to enhance the quality and effectiveness of health care delivery.

SARS-Cov-2 is not to be introduced anymore. The global pandemic that originated more than a year ago in Wuhan, China has claimed thousands of lives. Since the arrival of this plague, face mask has become part of our dressing code. The focus of this study is to design, develop and evaluate a COVID-19 fatality rate classifier at the county level. The proposed model predicts fatality rate as low, moderate, or high. This will help government and decision makers to improve mitigation strategy and provide measures to reduce the spread of the disease. Tourists and travelers will also find the work useful in planning of trips. Dataset used in the experiment contained imbalanced fatality levels. Therefore, class imbalance was offset using SMOTE. Evaluation of the proposed model was based on precision, F1 score, accuracy, and ROC curve. Five learning algorithms were trained and evaluated. Experimental results showed the Bagging model has the best performance.

COVID-19 is an infectious disease caused by the SARS-COV-2 coronavirus, which was discovered in late 2019. Within a few months, COVID-19 was declared a global pandemic by the WHO. Several countries adopted social distancing measures, such as self-quarantine, workplace and mobility restrictions, reducing the probability of contact between non-infected and infected people. In general, these measures have a negative impact on low-income economies and small and medium businesses. During the outbreak, several predictive models have been proposed in order to assess the level of saturation that health services might have. Nevertheless, none of them considers information on the peoples mobility to assess the effectiveness of the social distancing policies. In this study, the authors propose a prediction method based on peoples open mobility data from Apple(C) and Google(C) databases to project potential scenarios and monitor case growth. The proposed method shows the importance of monitoring daily case increase for the first 4-6 weeks of the pandemic wave. Active monitoring is crucial to determine the reduction in mobility and proper actions. The results can contribute to health authorities for making timely decisions, preventing the spread of viruses while balancing the reduction of mobility with minimal disruption in peoples economies in future outbreaks.

This study presents a bibliometric analysis of publications on mental health during and post-COVID-19 pandemic in Malaysia. The dataset comprises 167 documents retrieved from the Scopus database covering the period from 2020 to 2022. Using the open-source tool Bibliometrix for science mapping analysis, we examined various aspects of these publications, including document type, number of articles, total citations, most relevant sources, source impact, most relevant authors, affiliations, globally cited documents, and a word cloud. Our findings reveal a significant and continuous growth in mental health-related publications since the onset of the pandemic in 2020, highlighting an intensified focus on this critical area. This surge in research emphasizes the heightened importance of understanding and addressing mental health issues exacerbated by the pandemic. By providing a comprehensive summary of the bibliometric data, this study enhances our understanding of publication trends and the evolving landscape of mental health research, offering valuable insights for researchers, policymakers, and practitioners aiming to respond to mental health challenges in the post-pandemic era.

Covid-19, just like SARS and MERS before it, is a disease caused by corona virus and can lead to severe respiratory diseases in humans. With the outbreak of novel corona virus, WHO on 30th January 2020 declared it a Public Health Emergency and further on 11th March 2020, Covid-19 disease was declared a pandemic. India in the initial stages of the pandemic dealt with it in a very effective manner. With timely implementation of lockdown, India was able to contain the spread of Covid-19 to some extent. However with the recently announced Unlock 1.0, the SARS CoV-2 is expected to spread. This study aims to track and analyze the Covid-19 situation in major states that constitute of 70 percent of the total cases. Thus the states selected for the study are: Maharashtra, Delhi, Tamil Nadu, Gujarat, Uttar Pradesh and Rajasthan. These are the states which had more than ten thousand Covid-19 patients as/on June 10th 2020. The analysis period is from March 25th to June 10th and the data source is Indias Covid-19 tracker. To assess the previous and current Covid-19 situations in these states indicators such as Active rates, Recovery rate, Case fatality rate, Test positivity rate, tests per million, cases per million, test per confirmed case has been used. The study finds that although the absolute number of active cases may be rising, however it is showing a decreasing trend with an increase in recovery rates. With increasing number of Covid-19 cases, testing also has increased however not in the similar proportion and thus by developed nation standard we are lagging. With increasing TPR and cases per million, Delhi is well on its way to surpass even Mumbai which till now has proven to be worst hit in this pandemic. An interesting finding is that of test per confirmed case which shows that every 6th person in Maharashtra and every 8th in Delhi is showing positive result of Covid-19 test. Given such an increase and unlocked India, Delhi might soon enter into the third stage of community transmission where source of 50 percent or more cases would be unknown. There has been an increase in the Covid-19 related health infrastructure with the public-private partnership which involved both private hospitals and lab joining hands to battle Covid-19, however, affordability still remains an issue. If experts are to be believed, pandemic isnt over because weve unlocked. The worst is yet to come as Covid-19 is predicted to peak in mid-July to August in India. Thus, itd be advisable to not venture out unnecessarily just because restrictions have been lifted. Also, following the guidelines-hand-washing, avoiding public gathering, social distancing and covering nose and mouth has now become imperative.