Presenting a New Strategy to Extract Data Clustering Heartbeat Samples by Using Discrete Wavelet Transform

This paper presents the improvement of detection system that normal and arrhythmia electrocardiogram classification. This classification is done to aid the ANFIS (Adaptive Neuro Fuzzy Inference System). The data used in this paper obtained from MIT-BIH normal sinus ECG database signal and MIT-BIH arrhythmia database signal. The main goal of our approach is to create an interpretable classifier that provides an acceptable accuracy. In this model, the feature extraction using DWT (Discrete Wavelet Transform) is obtained. The last stage of this extraction is introduced as the input of ANFIS model. In this paper, the ANFIS model has been trained with Quantum Behaved Particle Swarm Optimization (QPSO). In this study, for training of proposed model, four sample data have been used which result in acceleration of training data. On the test set, we achieved an outstanding sensitivity and accuracy 100%. Experimental results show that the proposed approach is very fast and accurate in improving classification. Using the proposed methodology and telemedicine technology can manage patient of heart disease

Presenting a New Strategy to Extract Data Clustering Heartbeat Samples by Using Discrete Wavelet Transform

This paper presents the improvement of detection system that normal and arrhythmia electrocardiogram classification. This classification is done to aid the ANFIS (Adaptive Neuro Fuzzy Inference System). The data used in this paper obtained from MIT-BIH normal sinus ECG database signal and MIT-BIH arrhythmia database signal. The main goal of our approach is to create an interpretable classifier that provides an acceptable accuracy. In this model, the feature extraction using DWT (Discrete Wavelet Transform) is obtained. The last stage of this extraction is introduced as the input of ANFIS model. In this paper, the ANFIS model has been trained with Quantum Behaved Particle Swarm Optimization (QPSO). In this study, for training of proposed model, four sample data have been used which result in acceleration of training data. On the test set, we achieved an outstanding sensitivity and accuracy 100%. Experimental results show that the proposed approach is very fast and accurate in improving classification. Using the proposed methodology and telemedicine technology can manage patient of heart disease

Challenges and Barriers to Providing Care to Older Adult Patients in the Intensive Care Unit: A Qualitative Research

BACKGROUND: Enhancing the quality of care for elderly patients needs an understanding of the challenges and obstacles experienced by the intensive care unit (ICU) staff in providing care. AIM: To explore the most challenging issues experienced by ICU staff, in particular, nurses, in the care of elderly patients in the general adult ICU. DESIGN: A qualitative research design was employed. The Standards for Reporting Qualitative Research (SRQR) were followed. METHODS: Based on theoretical sampling, we carried out 34 in-depth semi-structured interviews from two medical adult ICUs. Data analysis was carried out using qualitative conventional content analysis. RESULTS: Data analysis led to the identification of three interrelated categories and 12 subcategories. Three main categories were factors related to nurses’ attitude in elderly care, factors related to the system of care, and factors related to the models of patient care delivery. These categories came under the main theme of "Inappropriate and unfair system for elderly care". CONCLUSION: The findings of this study increase scholarly understanding of challenges and barriers to providing care to elderly patients in the general adult ICU. We found that the provision of care to elderly patients is inappropriate and unfair. Various obstacles must be overcome to improve the care of these patients. For example, negative attitudes toward elder care, inappropriate environments, lack of resources, lack of knowledge and skills, a specialized model of care delivery, respect for humanity, care without considering patient age, and separating professional conflicts from patient care. These findings may be used by ICU’s caregivers and managers to improve the quality of care. IMPLICATIONS FOR PRACTICE: Various obstacles were documented that need to be overcome by hospital administrators, nursing managers, clinical nurses, nursing educators, nursing researchers to improve the care of elderly patients admitted to ICU

Stock market forecasting using artificial neural networks

Forecasting events has always been of great interest for human beings. The basic examples of this process are forecasting the weather and environmental disasters. To forecast is the process of collecting information in order to complete and expand them suitably for future. Today, globalization of economic and competes in this regard for observing investors and recognition of profit making and trusting markets, such as currency and stock market, which are highly complex, is now one of the most important umbrages of investors. For forecasting in capital markets such as stock or currency, there exist different methods, like, regression, time series, genetics algorithm and fundamental analysis. From non-liner methods which might be used in different forecasting bases are Artificial Neural Networks ANN. ANN are one of the newest inventions of mankind which are used in variety of different scientific fields. Use of investors of technology and computer algorithms for forecasting has caused more profit and better business opportunities. ANN is a part of dynamic systems which by processing on data of time series, drive the roles and science of these data and register it with the structure of the network. This system is based on computational intelligence which copies the human’s mind feature in processing. In this survey, besides discussing the ANN for analyzing and processing data and also studying new methods, it is concluded that ANN are an appropriate model for forecasting capital markets such as stock and currency

Stock market forecasting using artificial neural networks

Forecasting events has always been of great interest for human beings. The basic examples of this process are forecasting the weather and environmental disasters. To forecast is the process of collecting information in order to complete and expand them suitably for future. Today, globalization of economic and competes in this regard for observing investors and recognition of profit making and trusting markets, such as currency and stock market, which are highly complex, is now one of the most important umbrages of investors. For forecasting in capital markets such as stock or currency, there exist different methods, like, regression, time series, genetics algorithm and fundamental analysis. From non-liner methods which might be used in different forecasting bases are Artificial Neural Networks ANN. ANN are one of the newest inventions of mankind which are used in variety of different scientific fields. Use of investors of technology and computer algorithms for forecasting has caused more profit and better business opportunities. ANN is a part of dynamic systems which by processing on data of time series, drive the roles and science of these data and register it with the structure of the network. This system is based on computational intelligence which copies the human’s mind feature in processing. In this survey, besides discussing the ANN for analyzing and processing data and also studying new methods, it is concluded that ANN are an appropriate model for forecasting capital markets such as stock and currency

Investigating the effect of the CPOT-based pain management program on the pain intensity and dose adjustment of analgesics in mechanically ventilated patients: A randomized clinical trial

Background & Aim: This research was conducted to determine the effect of a pain management program based on the Critical Care Pain Observation Tool (CPOT) on the pain intensity and adjusting the dosage of analgesics in mechanically ventilated patients hospitalized in intensive care units. Methods & Materials: This randomized controlled clinical trial was conducted during 2019-2022 on 70 mechanically ventilated patients admitted to ICUs of the Imam Reza Hospital, Mashhad, Iran. In the intervention group, patients' pain intensity was measured during endotracheal suctioning using CPOT. Then the dosage of analgesics was adjusted based on the assessed pain level. The pain level was evaluated in the control group according to the department's routine. Data were analyzed using two-way repeated measures analysis of variance (RMANOVA). Results: CPOT and BPS showed a strong correlation (r>0.9, Pvalue<0.001) between the two pain intensity instruments. Based on the results of RMANOVA, a significant trend of pain intensity measures was observed during all three suctions (measurement effect P<0.05), which was different between the two groups (interaction effect P<0.05). The Sidak post hoc test results showed a significant difference in pain intensity measures between the intervention and control groups during all three suctions and 5 and 15 minutes after suctions (all P-values<0.05). Also, significant intervention effects were observed regarding the amount of analgesics prescribed in terms of total fentanyl blouse, total infused Fentanyl, and total Fentanyl (all P-values<0.05). Conclusion: Using CPOT is a positive step in the evaluation and control of dosage adjustment of analgesic medications for patients with mechanical ventilation

Using Intra-Aortic Balloon Pump for Management of Cardiogenic Shock Following Aluminum Phosphide Poisoning; Report of 3 Cases

Introduction: Aluminium phosphide (Alp) poisoning mortality rate has been reported as high as 70-100%, and refractory hypotension and cardiogenic shock are the two most common presentations leading to death. Due to lack of specific antidote, all treatments are focused on supportive care and recently, intra-aortic balloon pump (IABP) has been suggested to treat cardiogenic shock resulting from toxic myocarditis. In the current paper, we introduce three Alp poisoned patients for whom IABP was applied to manage their refractory shock. Case presentation: Two men and one woman who were admitted to emergency department (ED) of Imam Reza academic Hospital, Mashhad, Iran due to intentional Alp poisoning are reported. The cases visited the ED shortly after ingestion and nearly all of them showed hypotension, tachycardia and metabolic acidosis during early hospitalization. Due to persistent shock state, despite receiving intravenous fluid therapy and vasopressor agents, IABP insertion was performed in these cases. Finally, one of them survived and the other two died. Conclusion: It still cannot be decided whether IABP insertion is effective in cases of Alp poisoning or not. It might be reasonable to try this intervention along with other conservative treatments in patients who survive more than 12 hours and consistently suffer from refractory hypotension

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic