The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19

Li, Jing; Wang, Lishi; Guo, Sumin; Xie, Ning; Yao, Lan; Cao, Yukun; Day, Sara W.; Howard, Scott C.; Graff, J. Carolyn; Gu, Tianshu; Ji, Jiafu; Gu, Weikuan; Sun, Dianjun

doi:10.1016/j.dib.2020.105619

Cited by 11 publications

(6 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mortality rate during the pandemic has exceeded the expected mortality rate for the same period of time by almost 70% (Ministry of Health, Consumption, & Social Welfare, 2020). The severity of the complications of the disease and its high mortality rates cause great suffering and result in patients having to be hospitalised and receive specialised care (Baud et al., 2020; Li et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

Compassion fatigue, burnout, compassion satisfaction and perceived stress in healthcare professionals during the COVID‐19 health crisis in Spain

Fernández

Ramos‐Pichardo

Ibáñez‐Masero

et al. 2020

Journal of Clinical Nursing

391

366

View full text Add to dashboard Cite

Aims and objectives: To evaluate compassion fatigue (CF), burnout (BO), compassion satisfaction (CS) and perceived stress in healthcare professionals during the coronavirus disease 2019 (COVID-19) health crisis in Spain. Background: Spain has been one of the countries hardest hit by the health crisis caused by the COVID-19 pandemic. Healthcare professionals have had to deal with traumatic and complex situations in the work context. In these particularly stressful situations, many professionals may develop CF or BO, which puts them at risk for mental health problems. Design: Cross-sectional online survey. Methods: A total of 506 healthcare professionals (physicians and nurses) who were working in healthcare centres during the COVID-19 pandemic participated. CF, CS and BO were assessed with the Professional Quality of Life Questionnaire, and perceived stress was measured with the Perceived Stress Scale-14. Socio-demographic and occupational variables were also analysed. Data were collected during the period of the highest incidence of cases and highest mortality rates due to COVID-19 in Spain. This article adheres to the STROBE guidelines for the reporting of observational studies. Results: Physicians had higher CF and BO scores, while nurses had higher CS scores. Perceived stress scores were similar in both occupations. Professionals working in specific COVID-19 units and in emergency departments had higher CF and BO scores, while levels of CS and perceived stress were similar regardless of the workplace. Relevance to clinical practice: Despite the health crisis situation and its implications for healthcare professionals, the levels of CF and BO have remained moderate/high. However, CS seems to be increasing, especially among nurses, possibly due to their motivation to relieve suffering and due to their perceived social recognition. It is

show abstract

Section: Introductionmentioning

confidence: 99%

Compassion fatigue, burnout, compassion satisfaction and perceived stress in healthcare professionals during the COVID‐19 health crisis in Spain

Fernández

Ramos‐Pichardo

Ibáñez‐Masero

et al. 2020

Journal of Clinical Nursing

391

366

View full text Add to dashboard Cite

show abstract

“…In the present section, the developed gamma control chart using GMDS sampling is applied to the monitoring of Coronavirus (COVID-19) outbreak in China. The data set is borrowed from Li et al [ 38 ], and they discussed mortality caused by COVID-19 based on collected data of 33 death cases in Wuhan city of Hubei province during an early outbreak as well as confirmed cases and death toll. They have studied the COVID-19 outbreak in China by considering some specific regions as representative samples.…”

Section: Comparison With Existing Chartsmentioning

confidence: 99%

Monitoring Mortality Caused by COVID-19 Using Gamma-Distributed Variables Based on Generalized Multiple Dependent State Sampling

Aslam

Rao

Sherwani

2021

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

More recently in statistical quality control studies, researchers are paying more attention to quality characteristics having nonnormal distributions. In the present article, a generalized multiple dependent state (GMDS) sampling control chart is proposed based on the transformation of gamma quality characteristics into a normal distribution. The parameters for the proposed control charts are obtained using in-control average run length (ARL) at specified shape parametric values for different specified average run lengths. The out-of-control ARL of the proposed gamma control chart using GMDS sampling is explored using simulation for various shift size changes in scale parameters to study the performance of the control chart. The proposed gamma control chart performs better than the existing multiple dependent state sampling (MDS) based on gamma distribution and traditional Shewhart control charts in terms of average run lengths. A case study with real-life data from ICU intake to death caused by COVID-19 has been incorporated for the realistic handling of the proposed control chart design.

show abstract

“…Kabir and Peter [2] , [3] show the online forecasting mechanism for every 24 h and survey about risk perception and health behavior among Nigerian using Nigerian Government Data. Jing [4] presented a dataset containing 33-death case information from Wuhan for estimating death rate while Marcel [5] presented a combinational dataset of country profile and mobility analysis. Two separate datasets are brought by Cam-Tu [6] , [7] about Vietnamese students and teachers' learning habits and perceived support during COVID-19.…”

Section: Data Descriptionmentioning

confidence: 99%

Covid-19 Dataset: Worldwide spread log including countries first case and first death

et al. 2020

View full text Add to dashboard Cite

The article represents coronavirus spread log history. The duration, coronavirus takes to spread from one country to another country, could be seen in this dataset and could predicted the same for future pandemics through this dataset. It is highly dependent on the cabalistic number of variables that is the main navel of these datasets. Information for this dataset is collected from trusted websites, local and international popular newspapers. This coronavirus dataset not only help to track the spreading route of coronavirus but also can be used for predicting the possible spreading route of similar future pandemics. This dataset consists of 186 countries' useful data related to COVID-19 pandemic from November 17, 2019, to May 16, 2020, with 8 unique variables that provide the information of the nature of the spread of COVID-19. The datasets mainly focus on two major fields, first one is First Case which consists of information of Date of First Case(s), Number of confirm Case(s) at First Day, Age of the patient(s) of First Case, Last Visited Country and the other one First Death information consist of Date of First Death and Age of the Patient who died first for every Country mentioning corresponding Continent.This dataset also can perform a bunch of predictions using Machine Learning applications, like -how fast the virus is spreading, affect rate, death rate, death rate and able to represent comparison between other pandemics. Using this dataset, any similar pandemic spreadness could be predicted earlier and necessary precaution measures could be taken.

show abstract

The Data set for Patient Information Based Algorithm to Predict Mortality Cause by COVID-19

Cited by 11 publications

References 5 publications

Compassion fatigue, burnout, compassion satisfaction and perceived stress in healthcare professionals during the COVID‐19 health crisis in Spain

Compassion fatigue, burnout, compassion satisfaction and perceived stress in healthcare professionals during the COVID‐19 health crisis in Spain

Monitoring Mortality Caused by COVID-19 Using Gamma-Distributed Variables Based on Generalized Multiple Dependent State Sampling

Covid-19 Dataset: Worldwide spread log including countries first case and first death

Contact Info

Product

Resources

About