Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 10 de 10
Filter
1.
Machine Learning for Healthcare Systems: Foundations and Applications ; : 109-129, 2023.
Article in English | Scopus | ID: covidwho-20241481

ABSTRACT

According to Chinese health officials, almost 250 million people in China may have caught Covid-19 in the first 20 days of December. Due to the Covid-19 pandemic and its global spread, there is a significant impact on our health system and economy, causing many deaths and slowing down worldwide economic progress. The recent pandemic continues to challenge the health systems worldwide, including a life that realizes a massive increase in various medical resource demands and leads to a critical shortage of medical equipment. Therefore, physical and virtual analysis of day-to-day death, recovery cases, and new cases by accurately providing the training data are needed to predict threats before they are outspread. Machine learning algorithms in a real-life situation help the existing cases and predict the future instances of Covid-19. Providing accurate training data to the learning algorithm and mapping between the input and output class labels minimizes the prediction error. Polynomials are usually used in statistical analysis. Furthermore, using this statistical information, the prediction of upcoming cases is more straightforward using those same algorithms. These prediction models combine many features to predict the risk of infection being developed. With the help of prediction models, many areas can be strengthened beforehand to cut down risks and maintain the health of the citizens. Many predictions before the second wave of Covid-19 were realized to be accurate, and if we had worked on it, we would have decreased the fatality rate in India. In particular, nine standard forecasting models, such as linear regression (LR), polynomial regression (PR), support vector machine (SVM), Holt's linear, Holt-Winters, autoregressive (AR), moving average (MA), seasonal autoregressive integrated moving average (SARIMA), and autoregressive combined moving average (ARIMA), are used to forecast the alarming factors of Covid-19. The models make three predictions: the number of new cases, deaths, and recoveries over the next 10 days. To identify the principal features of the dataset, we first grouped different types of cases as per the date and plotted the distribution of active and closed cases. We calculated various valuable stats like mortality and recovery rates, growth factor, and doubling rate. Our results show that the ARIMA model gives the best possible outcomes on the dataset we used with the most minor root mean squared error of 23.24, followed by the SARIMA model, which offers somewhat close results to the AR model. It provides a root mean square error (RMSE) of 25.37. Holt's linear model does not have any considerable difference with a root mean square error of 27.36. Holt's linear model has a value very close to the moving average (MA) model, which results in the root mean square of 27.43. This research, like others, is also not free from any shortcomings. We used the 2019 datasets, which missed some features due to which models like Facebook Prophet did not predict results up to the mark;so we excluded those results in our outcomes. Also, the python package for the Prophet is a little non-functional to work on massive Covid-19 datasets appropriately. The period is better, where there is a need for more robust features in the datasets to support our framework. © 2023 River Publishers.

2.
Otorhinolaryngology Clinics ; 14(2):56-59, 2022.
Article in English | EMBASE | ID: covidwho-1917984

ABSTRACT

Importance: There is a need to identify the implications of the loss of smell and taste in cases of coronavirus disease-2019 (COVID-19). Objective: To determine whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is causing isolated anosmia in adult population and whether there is a role of intranasal corticosteroids (INCs) in cases of olfactory dysfunctions (ODs). Design: This was a prospective, cross-sectional, questionnaire-based study of 416 patients diagnosed with COVID-19 in a single institute. Setting: Dedicated COVID-19 facility. Participants: All patients had been tested for COVID-19 using a reverse transcription–polymerase chain reaction (RT-PCR)-based test. Patients who were hospitalized were approached in person. All patients who were discharged were then contacted by telephone up to two times to complete the study. Patients who were not reachable with two telephone calls were excluded. Demographic characteristics of the participants—age, sex, and smoking history—were collected. A standardized questionnaire was given to participants. Result: Olfactory dysfunctions (ODs) was observed in 58 patients and isolated anosmia in 3.6% of patients;82.7% showed complete recovery of smell, and 6.9% had partial recovery following INCs. Conclusion: Patients reporting recent onset of anosmia should be considered positive for SARS-CoV-2 infection until proven otherwise by a screening polymerase chain reaction test. Also, ear, nose, and throat (ENT) surgeons in particular who see patients with new-onset anosmia during the COVID-19 pandemic must take safety measures to reduce the risk of exposure and infection of healthcare workers and recommend such patients for RT-PCR test. Females and young adults are more prone to SARS-CoV-2 infection. Early intervention by INCs could be beneficial in improving olfactory and taste dysfunctions (OTDs) and other post-viral neurological manifestations. It could be beneficial in improving the quality of life of elderly patients who are at a higher risk of permanent OTDs. Smokers are at a higher risk of OTDs, but this could be reversible after smoking cessation. There is a need to put SARS-CoV-2 as a differential diagnosis in cases of sudden isolated OTDs.

3.
2nd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2021 ; 431:189-198, 2022.
Article in English | Scopus | ID: covidwho-1872364

ABSTRACT

COVID-19 virus has been a worldwide pandemic since its outbreak from December 2019. While coronavirus has a low fatality rate, it is extremely infectious and escalates quickly;therefore, early detection is very important for preventing its outbreak. The procedures currently used by medical personals for detection is RT-PCR test. However, it includes false negative reports and also is a time taking process;thus an alternate solution is required. Any diagnostic system that can detect COVID-19 infection can be very helpful to medical personals. The features found in COVID-19 images by X-rays are very similar to other lung diseases, which makes it very difficult to differentiate. This review includes the contribution of image processing and machine learning to make swift and precise diagnostic system from lung X-ray images. Such a system can be used by radiologists for making decisions and can be very helpful in prior detection of the virus. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

4.
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery ; : 27, 2022.
Article in English | Web of Science | ID: covidwho-1850260

ABSTRACT

This article is dedicated to study the impact of machine intelligence (MI) methods viz. various types of Neural models for investigating dynamical systems arising in interdisciplinary areas. Different types of artificial neural network (ANN) methods, viz., recurrent neural network, functional-link neural network, convolutional neural network, symplectic artificial neural network, genetic algorithm neural network, and so on, are addressed by different researchers to investigate these problems. Although various traditional methods have been developed by researchers to solve these dynamical problems but the existing traditional methods may sometimes be problem dependent, require repetitions of the simulations, and fail to solve nonlinearity behavior. In this regard, neural network model based methods are more general and solutions are continuous over the given domain of integration, self-adaptive and can be used as a black box. As such, in this article, we have reviewed and analyzed different MI methods, which are applied to investigate these problems. This article is categorized under: Technologies > Computational Intelligence Technologies > Machine Learning Application Areas > Science and Technology

5.
Med J Armed Forces India ; 2022 Apr 04.
Article in English | MEDLINE | ID: covidwho-1773646

ABSTRACT

Background: This study was carried out to evaluate the effectiveness of partial and full vaccination with ChAdOx1 nCoV-19 (COVISHIELD) to prevent the development of moderate or severe illness among COVID-positive cases. Methods: This prospective cohort study was conducted among Armed Forces personnel deployed in Northern India who were found COVID positive during the study period between January and June 2021. Information about the vaccination status, age and comorbidities was collected at the time of diagnosis. Classification of COVID cases as moderate or severe was performed as per criteria given by the Government of India. Individuals were considered partially vaccinated three weeks after one dose and fully vaccinated two weeks after the second dose. Risk ratio and vaccine effectiveness (VE) to prevent moderate or severe disease among COVID cases were calculated. Results: A total of 2005 COVID-19 patients were included in our study. Partial vaccination and full vaccination with ChAdOx1 nCoV-19 offered 13% (95% credible interval (CI): -56.8%, 52.8%) and 66.6% (95% CI: 34.9%, 84.6%) protection against progression to moderate/severe illness among COVID-positive individuals. The risk of moderate-severe disease among COVID-positive cases occurring 4-11 weeks after the first dose was also lesser among those who had taken the second dose of vaccine than individuals who have been vaccinated with only one dose. Conclusion: Interval between the first and second doses of ChAdOx1 nCoV-19 vaccine should be reduced to 4-6 weeks, as partial vaccination offers lower protection against the development of moderate-severe illness after COVID infection.

6.
Aquaculture ; 548:N.PAG-N.PAG, 2022.
Article in English | Academic Search Complete | ID: covidwho-1544781

ABSTRACT

A detailed investigation on mass mortality of fishes was conducted in a small tropical reservoir- Derjang (20o50'32.0"N, 85o01'14.8″E), Odisha, India. Mortality mostly occurred in Systomus sarana followed by Labeo rohita , Cirrhinus mrigala , Labeo catla , Ompok bimaculatus , Labeo calbasu and Mastacembelus armatus. During 20 days of disease occurrence in May–June 2019, a total of about 3000 kg of fish died. The clinical signs in Cyprinid group were haemorrhagic spots, ulcerative lesions, rotten and pale patches in gills due to septicemic disease whereas haemorrhagic spots were the only prominent symptoms observed in Silurid group. Bacteriological isolation and identification through conventional and molecular techniques revealed that Klebsiella pneumoniae was the most common pathogen recovered from S. sarana , C. mrigala and O. bimaculatus. Further Aeromonas hydrophila , Acinetobacter baumannii were isolated from L. rohita and L. catla respectively. The role of these pathogens for this disease outbreak in multiple fish species is discussed in perspective of environmental factors. Sudden environmental alternation by the supercyclone Fani (3rd May 2019) on the coastal part of Odisha might have played a key role to translate the aquatic bacteria into the virulent infective pathogens. In the experimental challenge study, isolated bacteria showed pathogenicity in respective hosts as that in the reservoir. Thus this further revealed both bacteria and fish specific virulency with a variation in LD 50 values. All the gram negative bacterial isolates were found to resist ampicillin and amoxicillin-clavulanic acid and most of them were TEM gene positive. However, the bacteria were found to be susceptible to the rest of the nineteen antibiotics. These findings suggested that the sudden cyclone is an enormous threat to reservoir aquaculture, and should be taken into consideration before breeding, stocking and harvesting of fishes. • Disease outbreak in reservoir during Summer, 2019 caused mass mortality of many freshwater fishes with septicemic symptoms • Klebsiella pnumoniae was the most dominating pathogen recovered, besides Aeromonas hydrophila and Acinetobacter baumannii • Sudden environmental alternation by the cyclone Fani might have played a key role to flare up the virulent pathogens. • Specificity and virulence of bacteria were found host-dependent with variable LD 50 as revealed by challenge experiments [ FROM AUTHOR] Copyright of Aquaculture is the property of Elsevier B.V. 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.)

7.
Journal of Clinical and Diagnostic Research ; 15(11):NR1-NR4, 2021.
Article in English | Web of Science | ID: covidwho-1538821

ABSTRACT

There is an increased incidence of rhino orbital mucormycosis during the Coronavirus Disease-2019 (COVID-19) pandemic attributed to Diabetes Mellitus, corticosteroid usage and immunocompromise caused by COVID-19. In this series, seven biopsy-proven cases of mucormycosis (six male patients and 1 female patients) are presented from a tertiary center in eastern India from (May 2021 to June 2021). Empirical systemic liposomal amphotericin B, radical sinus surgery with orbital decompression and irrigation of sinus and orbit with amphotericin B was performed. The mean age of the patients was 42.71 +/- 7.34 years with a male preponderance (85.7%). Five patients had orbital involvement (71.42%), and two had cerebral involvement (28.6%). All of them had elevated blood glucose levels, though only three (42.85%) were known cases of type 2 diabetes. The most common manifestations were sinus tenderness (100%), paresthesia (100%), facial swelling (71.42%) and nasal discharge (28.57%). Follow-up at two months showed zero mortality. Timely diagnosis, appropriate management with intravenous amphotericin B and endoscopic radical sinus surgery, debridement of the necrotic tissue proved to be necessary for a good outcome in rhino-orbito-cerebral mucormycosis.

8.
Vine Journal of Information and Knowledge Management Systems ; ahead-of-print(ahead-of-print):16, 2021.
Article in English | Web of Science | ID: covidwho-1494251

ABSTRACT

Purpose The purpose of this paper is to investigate physicians' perceptions of e-consultation adoption, which has the potential to bridge existing gaps in the current health-care system, using the unified theory of acceptance and use of technology (UTAUT2) framework. Design/methodology/approach The judgemental sampling method was embraced to collect primary data from 337 physicians from Delhi-National Capital Region who had experience with e-consultation. A number of hypotheses was developed and tested using structural equation model based on UTAUT2. Findings The study's findings revealed an affirmative and significant relation between a physician's intention to embrace e-consultation and facilitating conditions, effort expectancy, social influence and performance expectancy;however, habit and experience are not significantly linked to it. Originality/value This study will not only add to the existing body of knowledge about e-consultation adoption, but it will also assist electronic health service providers in devising strategies to encourage the usage of e-consultation services in emerging economies such as India where people are deprived of the right to access better health care due to lack of physical infrastructure.

9.
International Journal of Pharmaceutical and Healthcare Marketing ; 2021.
Article in English | Scopus | ID: covidwho-1405104

ABSTRACT

Purpose: The purpose of this paper is to understand the factors influencing the adoption decision of patients towards digital consultation in India with gender as a moderating variable. This study is based on the unified theory of acceptance and use of technology (UTAUT2) framework for examining the factors influencing adoption decisions for digital consultation and to what extent this leads to continuous usage intention. Design/methodology/approach: Based on the UTAUT2 framework, this study proposed a set of hypotheses that were tested using structural equation modeling. This study was based on primary data collected from 462 sample respondents using the judgemental sampling method who had experience of using digital health consultation in India. Findings: Findings of this study revealed significant and positive causation in the behavioural intention (BI) of a patient to adopt digital health consultation arising out of performance expectancy, effort expectancy, social influence, facilitating condition and price value;however, habit is insignificantly associated with the same. Furthermore, the results of this study also revealed that the BI of a patient towards digital health consultation is significantly moderated by their gender. Originality/value: This study conceptually strengthens the present body of literature on the adoption behaviour by contributing certain new dimensions in the context of digital health consultations and will also help policymakers and service providers in crafting their strategy for promoting the adoption of digital health consultation. © 2021, Emerald Publishing Limited.

SELECTION OF CITATIONS
SEARCH DETAIL