scholarly journals Genetic‐based adaptive momentum estimation for predicting mortality risk factors for COVID ‐19 patients using deep learning

2021 ◽  
Author(s):  
Sally M. Elghamrawy ◽  
Aboul Ella Hassanien ◽  
Athanasios V. Vasilakos
Keyword(s):  
Risk Factors ◽  
Deep Learning ◽  
Mortality Risk ◽  
Adaptive Momentum

Assessment of the Risk Factors of MDD Recurrence Based on Deep Learning Approaches

2019 ◽  
Author(s):  
Qian Wu ◽  
Weiling Zhao ◽  
Xiaobo Yang ◽  
Hua Tan ◽  
Lei You ◽  
...  
Keyword(s):  
Risk Factors ◽  
Deep Learning ◽  
Learning Approaches

scholarly journals Burden of Respiratory Syncytial Virus Disease and Mortality Risk Factors in Argentina

2019 ◽  
Vol 38 (6) ◽  
pp. 589-594 ◽  
Author(s):  
Angela Gentile ◽  
María Florencia Lucion ◽  
María del Valle Juarez ◽  
María Soledad Areso ◽  
Julia Bakir ◽  
...  
Keyword(s):  
Risk Factors ◽  
Respiratory Syncytial Virus ◽  
Mortality Risk ◽  
Virus Disease ◽  
Respiratory Syncytial Virus Disease ◽  
Syncytial Virus

Mortality Risk Factors in Chronic Renal Failure Patients after Coronary Artery Bypass Grafting

Renal Failure ◽  
2007 ◽  
Vol 29 (7) ◽  
pp. 823-828 ◽  
Author(s):  
Beril Akman ◽  
Ayse Bilgic ◽  
Gulsah Sasak ◽  
Siren Sezer ◽  
Atilla Sezgin ◽  
...  
Keyword(s):  
Risk Factors ◽  
Renal Failure ◽  
Coronary Artery ◽  
Chronic Renal Failure ◽  
Coronary Artery Bypass Grafting ◽  
Coronary Artery Bypass ◽  
Mortality Risk ◽  
Artery Bypass ◽  
Bypass Grafting ◽  
Artery Bypass Grafting

scholarly journals 699 Sleep Health Traits and COVID-19: Mortality Risk from the UK Biobank

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A273-A273
Author(s):  
Xi Zheng ◽  
Ma Cherrysse Ulsa ◽  
Peng Li ◽  
Lei Gao ◽  
Kun Hu
Keyword(s):  
Risk Factors ◽  
Sleep Apnea ◽  
Sleep Duration ◽  
Sex Education ◽  
Mortality Risk ◽  
Self Report ◽  
Severe Illness ◽  
Uk Biobank ◽  
Sleep Health ◽  
The Uk

Abstract Introduction While there is emerging evidence for acute sleep disruption in the aftermath of coronavirus disease 2019 (COVID-19), it is unknown whether sleep traits contribute to mortality risk. In this study, we tested whether earlier-life sleep duration, chronotype, insomnia, napping or sleep apnea were associated with increased 30-day COVID-19 mortality. Methods We included 34,711 participants from the UK Biobank, who presented for COVID-19 testing between March and October 2020 (mean age at diagnosis: 69.4±8.3; range 50.2–84.6). Self-reported sleep duration (less than 6h/6-9h/more than 9h), chronotype (“morning”/”intermediate”/”evening”), daytime dozing (often/rarely), insomnia (often/rarely), napping (often/rarely) and presence of sleep apnea (ICD-10 or self-report) were obtained between 2006 and 2010. Multivariate logistic regression models were used to adjust for age, sex, education, socioeconomic status, and relevant risk factors (BMI, hypertension, diabetes, respiratory diseases, smoking, and alcohol). Results The mean time between sleep measures and COVID-19 testing was 11.6±0.9 years. Overall, 5,066 (14.6%) were positive. In those who were positive, 355 (7.0%) died within 30 days (median = 8) after diagnosis. Long sleepers (>9h vs. 6-9h) [20/103 (19.4%) vs. 300/4,573 (6.6%); OR 2.09, 95% 1.19–3.64, p=0.009), often daytime dozers (OR 1.68, 95% 1.04–2.72, p=0.03), and nappers (OR 1.52, 95% 1.04–2.23, p=0.03) were at greater odds of mortality. Prior diagnosis of sleep apnea also saw a two-fold increased odds (OR 2.07, 95% CI: 1.25–3.44 p=0.005). No associations were seen for short sleepers, chronotype or insomnia with COVID-19 mortality. Conclusion Data across all current waves of infection show that prior sleep traits/disturbances, in particular long sleep duration, daytime dozing, napping and sleep apnea, are associated with increased 30-day mortality after COVID-19, independent of health-related risk factors. While sleep health traits may reflect unmeasured poor health, further work is warranted to examine the exact underlying mechanisms, and to test whether sleep health optimization offers resilience to severe illness from COVID-19. Support (if any) NIH [T32GM007592 and R03AG067985 to L.G. RF1AG059867, RF1AG064312, to K.H.], the BrightFocus Foundation A2020886S to P.L. and the Foundation of Anesthesia Education and Research MRTG-02-15-2020 to L.G.

scholarly journals Understanding Demographic Risk Factors for Adverse Outcomes in COVID-19 Patients: Explanation of a Deep Learning Model

2021 ◽  
Author(s):  
Yijun Shao ◽  
Ali Ahmed ◽  
Angelike P. Liappis ◽  
Charles Faselis ◽  
Stuart J. Nelson ◽  
...  
Keyword(s):  
Risk Factors ◽  
Deep Learning ◽  
Learning Model ◽  
Adverse Outcomes ◽  
Demographic Risk ◽  
Demographic Risk Factors ◽  
Deep Learning Model

Mortality and associated risk factors in diabetics

1985 ◽  
Vol 110 (4_Suppl) ◽  
pp. S21-S26 ◽  
Author(s):  
R. J. Jarrett ◽  
M. J. Shipley
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Mortality Risk ◽  
Mortality Rates ◽  
Potential Risk Factors ◽  
Diabetic Population ◽  
Associated Risk Factors ◽  
The Relationship

Summary. In 168 male diabetics aged 40-64 years participating in the Whitehall Study, ten-year age adjusted mortality rates were significantly higher than in non-diabetics for all causes, coronary heart disease, all cardiovascular disease and, in addition, causes other than cardiovascular. Mortality rates were not significantly related to known duration of the diabetes. The predictive effects of several major mortality risk factors were similar in diabetics and non-diabetics. Excess mortality rates in the diabetics could not be attributed to differences in levels of blood pressure or any other of the major risk factors measured. Key words: diabetics; mortality rates; risk factors; coronary heart disease. There are many studies documenting higher mortality rates - particularly from cardiovascular disease -in diabetics compared with age and sex matched diabetics from the same population (see Jarrett et al. (1982) for review). However, there is sparse information relating potential risk factors to subsequent mortality within a diabetic population, information which might help to explain the increased mortality risk and also suggest preventive therapeutic approaches. In the Whitehall Study, a number of established diabetics participated in the screening programme and data on mortality rates up to ten years after screening are available. We present here a comparison of diabetics and non-diabetics in terms of relative mortality rates and the influence of conventional risk factors as well as an analysis of the relationship between duration of diabetes and mortality risk.

scholarly journals Examination of "Early Mortality Exclusion" as an Approach to Control for Confounding by Occult Disease in Epidemiologic Studies of Mortality Risk Factors

1997 ◽  
Vol 146 (8) ◽  
pp. 672-680 ◽  
Author(s):  
D. B. Allison ◽  
M. Heo ◽  
D. W. Flanders ◽  
M. S. Faith ◽  
D. F. Williamson
Keyword(s):  
Risk Factors ◽  
Mortality Risk ◽  
Epidemiologic Studies ◽  
Early Mortality ◽  
Occult Disease

scholarly journals Status epilepticus management and mortality risk factors: a retrospective study

2021 ◽  
Author(s):  
M. Hidalgo de la Cruz ◽  
J.A. Miranda Acuña ◽  
E. Luque Buzo ◽  
B. Chavarria Cano ◽  
E. Esteban de Antonio ◽  
...  
Keyword(s):  
Risk Factors ◽  
Retrospective Study ◽  
Status Epilepticus ◽  
Mortality Risk
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