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1.
Lecture Notes in Networks and Systems ; 498:131-140, 2023.
Article in English | Scopus | ID: covidwho-2245089

ABSTRACT

Automated Patient monitoring is rising to importance in the mobile healthcare services as it makes day-to-day activities risk-free, by continuously monitoring their vital signs. Clinical solutions are being provided to patients in no time, which is made possible due to the latest improvements in the "Internet of Things (IoT), cloud computing, and fog computing”. "Machine learning and Deep learning” are now being extensively used for various applications in healthcare such as extracting relations from vast amounts of patient data, analyzing patterns to predict the propagation of diseases, classify reports and X-rays to detect diseases, to name a few. In this paper, a deep learning-based model is proposed to monitor Covid-affected patients within hospitals. Our model can provide an online link between a patient and medical facility while also collecting patient data. This will enhance the care taken for patients. At the hospital end, we present a deep learning model using ResNet-50 that could classify chest X-rays as Covid positive or No Covid. Through this model we expect to quicken the process of COVID-19 detection while lowering the healthcare expenses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

2.
Pakistan Journal of Medical and Health Sciences ; 16(11):468-471, 2022.
Article in English | EMBASE | ID: covidwho-2207096

ABSTRACT

Objective: To study the relationship of demographic factors and co-morbidities with post-COVID-19 recovery in tertiary care hospitals of Peshawar. Methodology: This research was conducted in tertiary care hospitals of Khyber Pakhtunkhwa extending over a period of 6 months starting from 1st July 2020 to 31st December 2020. It was an analytical descriptive study (cross-sectional). Patients were selected through a non-probability consecutive sampling technique. Descriptive statistics were performed with SPSS software 22.0 in the form of the mean (sd) and percentages while univariate and multivariate logistic regression scrutiny was performed with STATA version 13.0. Result(s): A mean age (48.94+/-17.57) was observed up to the post-infection recovery or death. The mean age of post-infection recovered patients in the age series of 18-35 years and >55 years was significantly significant (P<0.05) Out of those who recovered, 110 (79.5%) were males and 49(20.5%) were females while those who died of the infection 15(36.6%) were females and 26(83.8%) were males. Univariate analysis showed that age, residence, hypertension, and ischemic heart disease were the covariates significantly associated (p.value <0.05) with post COVID recovery. In multivariate analysis with adjusted OR, "residence" was the only covariate associated with post-infection recovery. Adjusting for the effect of age, gender, hypertension, diabetes, ischemic heart disease, those who were living in urban areas were most likely to recover from COVID-19 infection as compared to the peri-urban residents (OR=0.067, CI: 0.013-0.333). In the full deduced model, adjusting for age, gender, diabetes, hypertension and ischemic heart disease, being an urban resident was 0.08 times more likely to survive or alive after getting COVID-19 infection as compared to dwellers living in city outskirts (OR=0.08, CI: 0.016-0.360). Conclusion Patients suffering from chronic hypertension and ischemic heart diseases were the most affected having higher post-infection mortalities compared to diabetic patients while, from a demographic point of view, being a resident of an urban area was a protective factor for post-infection recovery. Copyright © 2022 Lahore Medical And Dental College. All rights reserved.

3.
1st International Conference on Information and Communication Technology, ICICT 2021 ; 498:131-140, 2023.
Article in English | Scopus | ID: covidwho-2148686

ABSTRACT

Automated Patient monitoring is rising to importance in the mobile healthcare services as it makes day-to-day activities risk-free, by continuously monitoring their vital signs. Clinical solutions are being provided to patients in no time, which is made possible due to the latest improvements in the “Internet of Things (IoT), cloud computing, and fog computing”. “Machine learning and Deep learning” are now being extensively used for various applications in healthcare such as extracting relations from vast amounts of patient data, analyzing patterns to predict the propagation of diseases, classify reports and X-rays to detect diseases, to name a few. In this paper, a deep learning-based model is proposed to monitor Covid-affected patients within hospitals. Our model can provide an online link between a patient and medical facility while also collecting patient data. This will enhance the care taken for patients. At the hospital end, we present a deep learning model using ResNet-50 that could classify chest X-rays as Covid positive or No Covid. Through this model we expect to quicken the process of COVID-19 detection while lowering the healthcare expenses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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