LIVIVO - Search results -

Search results

Result 1 - 10 of total 46

Search options

Book ; Online: Applications of Novel Analytical Methods in Epidemiology

VanderWaa, Kimberly / Brookes, Victoria J. / Alkhamis, Moh A.

2018

Keywords	Veterinary medicine ; Medicine (General)
Size	1 electronic resource (109 p.)
Publisher	Frontiers Media SA
Document type	Book ; Online
Note	English ; Open Access
HBZ-ID	HT020101957
ISBN	9782889456581 ; 2889456587
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

Order via subito

This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article: Editorial: Sequencing and phylogenetic analysis as a tool in molecular epidemiology of veterinary infectious diseases.

Leyson, Christina M / Alkhamis, Moh A / Goraichuk, Iryna V

Frontiers in veterinary science

2023 Volume 10, Page(s) 1236155

Language	English
Publishing date	2023-07-13
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2834243-4
ISSN	2297-1769
ISSN	2297-1769
DOI	10.3389/fvets.2023.1236155
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Interpretable machine learning models for predicting in-hospital and 30 days adverse events in acute coronary syndrome patients in Kuwait.

Alkhamis, Moh A / Al Jarallah, Mohammad / Attur, Sreeja / Zubaid, Mohammad

Scientific reports

2024 Volume 14, Issue 1, Page(s) 1243

Abstract: The relationships between acute coronary syndromes (ACS) adverse events and the associated risk factors are typically complicated and nonlinear, which poses significant challenges to clinicians' attempts at risk stratification. Here, we aim to explore ... ...

Abstract	The relationships between acute coronary syndromes (ACS) adverse events and the associated risk factors are typically complicated and nonlinear, which poses significant challenges to clinicians' attempts at risk stratification. Here, we aim to explore the implementation of modern risk stratification tools to untangle how these complex factors shape the risk of adverse events in patients with ACS. We used an interpretable multi-algorithm machine learning (ML) approach and clinical features to fit predictive models to 1,976 patients with ACS in Kuwait. We demonstrated that random forest (RF) and extreme gradient boosting (XGB) algorithms, remarkably outperform traditional logistic regression model (AUCs = 0.84 & 0.79 for RF and XGB, respectively). Our in-hospital adverse events model identified left ventricular ejection fraction as the most important predictor with the highest interaction strength with other factors. However, using the 30-days adverse events model, we found that performing an urgent coronary artery bypass graft was the most important predictor, with creatinine levels having the strongest overall interaction with other related factors. Our ML models not only untangled the non-linear relationships that shape the clinical epidemiology of ACS adverse events but also elucidated their risk in individual patients based on their unique features.
MeSH term(s)	Humans ; Acute Coronary Syndrome ; Stroke Volume ; Kuwait/epidemiology ; Ventricular Function, Left ; Hospitals ; Machine Learning
Language	English
Publishing date	2024-01-12
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-51604-8
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Interpretable machine learning models for predicting in-hospital and 30 days adverse events in acute coronary syndrome patients in Kuwait

Moh A. Alkhamis / Mohammad Al Jarallah / Sreeja Attur / Mohammad Zubaid

Scientific Reports, Vol 14, Iss 1, Pp 1-

2024 Volume 13

Abstract: Abstract The relationships between acute coronary syndromes (ACS) adverse events and the associated risk factors are typically complicated and nonlinear, which poses significant challenges to clinicians' attempts at risk stratification. Here, we aim to ... ...

Abstract	Abstract The relationships between acute coronary syndromes (ACS) adverse events and the associated risk factors are typically complicated and nonlinear, which poses significant challenges to clinicians' attempts at risk stratification. Here, we aim to explore the implementation of modern risk stratification tools to untangle how these complex factors shape the risk of adverse events in patients with ACS. We used an interpretable multi-algorithm machine learning (ML) approach and clinical features to fit predictive models to 1,976 patients with ACS in Kuwait. We demonstrated that random forest (RF) and extreme gradient boosting (XGB) algorithms, remarkably outperform traditional logistic regression model (AUCs = 0.84 & 0.79 for RF and XGB, respectively). Our in-hospital adverse events model identified left ventricular ejection fraction as the most important predictor with the highest interaction strength with other factors. However, using the 30-days adverse events model, we found that performing an urgent coronary artery bypass graft was the most important predictor, with creatinine levels having the strongest overall interaction with other related factors. Our ML models not only untangled the non-linear relationships that shape the clinical epidemiology of ACS adverse events but also elucidated their risk in individual patients based on their unique features.
Keywords	Medicine ; R ; Science ; Q
Subject code	610
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article: Anxiety disorders among children and adolescents during COVID-19 lockdowns and school closures: a cross-sectional study in Kuwait.

Alamiri, Bibi / Alkhamis, Moh A / Naguy, Ahmed / Alenezi, Hend F / Al Shekaili, Muna

Frontiers in psychiatry

2024 Volume 15, Page(s) 1322745

Abstract: Introduction: Investigating the epidemiology of mental health disorders resulting from COVID-19 intervention measures, primary school closures, and social isolation in children and adolescents needs to be prioritized over adults at the post-pandemic ... ...

Abstract	Introduction: Investigating the epidemiology of mental health disorders resulting from COVID-19 intervention measures, primary school closures, and social isolation in children and adolescents needs to be prioritized over adults at the post-pandemic stage. Most preliminary psychosocial studies conducted during the pandemic have demonstrated that younger age groups are the most vulnerable to such implications. Thus, this study aims to estimate the probable prevalence of specific anxiety disorders in children and quantify their relationships with relevant demographic risk factors. Methods: We used a cross-sectional study comprising 430 children aged between 8- and 18 years old living in Kuwait during the period of school closures as well as full and partial lockdowns. The survey included questions about participants' characteristics, children's anxiety using the Screen for Child Anxiety Related Emotional Disorders Questionnaire (SCARED) scale, and children's emotions and behaviours using the Strengths and Difficulties Questionnaire (SDQ). Univariate and multivariate logistic regression analyses were used to summarize the demographic and characteristics of the participants and their association with general, social, and generalized anxieties, as well as behavioural and emotional difficulties. Results: We inferred that 24.83% of our participants had at least one anxiety disorder, while 20.19% were classified as abnormal on the SDQ scale. Our multivariate analysis revealed that lockdown duration and sex of the child were consistently significant predictors (p-values < 0.05) of the broad spectrum of selected mental disorders. Additionally, we inferred notable increases in the likelihood of mental disorders associated with the increased duration of lockdowns. Conclusions: Our findings revealed preliminary insights into the vulnerability of young populations to the indirect negative impacts of strict public health measures during pandemic emergencies. Thus, authorities should consider such implications when planning and implementing similar interventions in future pandemics.
Language	English
Publishing date	2024-02-12
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2564218-2
ISSN	1664-0640
ISSN	1664-0640
DOI	10.3389/fpsyt.2024.1322745
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Editorial

Christina M. Leyson / Moh A. Alkhamis / Iryna V. Goraichuk

Frontiers in Veterinary Science, Vol

Sequencing and phylogenetic analysis as a tool in molecular epidemiology of veterinary infectious diseases

2023 Volume 10

Keywords	genome sequencing ; next-generation sequencing ; sanger sequencing ; phylogenetic analysis ; metagenomics ; molecular surveillance ; Veterinary medicine ; SF600-1100
Language	English
Publishing date	2023-07-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Psychological Disorders and Coping among Undergraduate College Students: Advocating for Students' Counselling Services at Kuwait University.

Alotaibi, Naser M / Alkhamis, Moh A / Alrasheedi, Mashael / Alotaibi, Khuloud / Alduaij, Latifa / Alazemi, Fatemah / Alfaraj, Danah / Alrowaili, Danah

International journal of environmental research and public health

2024 Volume 21, Issue 3

Abstract: Objectives: ...

Abstract	Objectives:
MeSH term(s)	Humans ; Universities ; Depression/epidemiology ; Depression/psychology ; Adaptation, Psychological ; Cross-Sectional Studies ; Kuwait/epidemiology ; Stress, Psychological/epidemiology ; Stress, Psychological/psychology ; Anxiety/epidemiology ; Anxiety/psychology ; Students/psychology ; Counseling
Language	English
Publishing date	2024-02-21
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2175195-X
ISSN	1660-4601 ; 1661-7827
ISSN (online)	1660-4601
ISSN	1661-7827
DOI	10.3390/ijerph21030245
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article: Animal Disease Surveillance in the 21st Century: Applications and Robustness of Phylodynamic Methods in Recent U.S. Human-Like H3 Swine Influenza Outbreaks.

Alkhamis, Moh A / Li, Chong / Torremorell, Montserrat

Frontiers in veterinary science

2020 Volume 7, Page(s) 176

Abstract: Emerging and endemic animal viral diseases continue to impose substantial impacts on animal and human health. Most current and past molecular surveillance studies of animal diseases investigated spatio-temporal and evolutionary dynamics of the viruses in ...

Abstract	Emerging and endemic animal viral diseases continue to impose substantial impacts on animal and human health. Most current and past molecular surveillance studies of animal diseases investigated spatio-temporal and evolutionary dynamics of the viruses in a disjointed analytical framework, ignoring many uncertainties and made joint conclusions from both analytical approaches. Phylodynamic methods offer a uniquely integrated platform capable of inferring complex epidemiological and evolutionary processes from the phylogeny of viruses in populations using a single Bayesian statistical framework. In this study, we reviewed and outlined basic concepts and aspects of phylodynamic methods and attempted to summarize essential components of the methodology in one analytical pipeline to facilitate the proper use of the methods by animal health researchers. Also, we challenged the robustness of the posterior evolutionary parameters, inferred by the commonly used phylodynamic models, using hemagglutinin (HA) and polymerase basic 2 (PB2) segments of the currently circulating human-like H3 swine influenza (SI) viruses isolated in the United States and multiple priors. Subsequently, we compared similarities and differences between the posterior parameters inferred from sequence data using multiple phylodynamic models. Our suggested phylodynamic approach attempts to reduce the impact of its inherent limitations to offer less biased and biologically plausible inferences about the pathogen evolutionary characteristics to properly guide intervention activities. We also pinpointed requirements and challenges for integrating phylodynamic methods in routine animal disease surveillance activities.
Keywords	covid19
Language	English
Publishing date	2020-04-21
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2834243-4
ISSN	2297-1769
ISSN	2297-1769
DOI	10.3389/fvets.2020.00176
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article: Comparative phylodynamics reveals the evolutionary history of SARS-CoV-2 emerging variants in the Arabian Peninsula.

Alkhamis, Moh A / Fountain-Jones, Nicholas M / Khajah, Mohammad M / Alghounaim, Mohammad / Al-Sabah, Salman K

Virus evolution

2022 Volume 8, Issue 1, Page(s) veac040

Abstract: Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continue to be responsible for an unprecedented worldwide public health and economic catastrophe. Accurate understanding and comparison of global and regional evolutionary ... ...

Abstract	Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continue to be responsible for an unprecedented worldwide public health and economic catastrophe. Accurate understanding and comparison of global and regional evolutionary epidemiology of novel SARS-CoV-2 variants are critical to guide current and future interventions. Here, we utilized a Bayesian phylodynamic pipeline to trace and compare the evolutionary dynamics, spatiotemporal origins, and spread of five variants (Alpha, Beta, Delta, Kappa, and Eta) across the Arabian Peninsula. We found variant-specific signatures of evolution and spread that are likely linked to air travel and disease control interventions in the region. Alpha, Beta, and Delta variants went through sequential periods of growth and decline, whereas we inferred inconclusive population growth patterns for the Kappa and Eta variants due to their sporadic introductions in the region. Non-pharmaceutical interventions imposed between mid-2020 and early 2021 likely played a role in reducing the epidemic progression of the Beta and the Alpha variants. In comparison, the combination of the non-pharmaceutical interventions and the rapid rollout of vaccination might have shaped Delta variant dynamics. We found that the Alpha and Beta variants were frequently introduced into the Arab peninsula between mid-2020 and early 2021 from Europe and Africa, respectively, whereas the Delta variant was frequently introduced between early 2021 and mid-2021 from East Asia. For these three variants, we also revealed significant and intense dispersal routes between the Arab region and Africa, Europe, Asia, and Oceania. In contrast, the restricted spread and stable effective population size of the Kappa and the Eta variants suggest that they no longer need to be targeted in genomic surveillance activities in the region. In contrast, the evolutionary characteristics of the Alpha, Beta, and Delta variants confirm the dominance of these variants in the recent outbreaks. Our study highlights the urgent need to establish regional molecular surveillance programs to ensure effective decision making related to the allocation of intervention activities targeted toward the most relevant variants.
Language	English
Publishing date	2022-05-18
Publishing country	England
Document type	Journal Article
ZDB-ID	2818949-8
ISSN	2057-1577
ISSN	2057-1577
DOI	10.1093/ve/veac040
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Anemia or other comorbidities? using machine learning to reveal deeper insights into the drivers of acute coronary syndromes in hospital admitted patients.

Alsayegh, Faisal / Alkhamis, Moh A / Ali, Fatima / Attur, Sreeja / Fountain-Jones, Nicholas M / Zubaid, Mohammad

PloS one

2022 Volume 17, Issue 1, Page(s) e0262997

Abstract: Acute coronary syndromes (ACS) are a leading cause of deaths worldwide, yet the diagnosis and treatment of this group of diseases represent a significant challenge for clinicians. The epidemiology of ACS is extremely complex and the relationship between ... ...

Abstract	Acute coronary syndromes (ACS) are a leading cause of deaths worldwide, yet the diagnosis and treatment of this group of diseases represent a significant challenge for clinicians. The epidemiology of ACS is extremely complex and the relationship between ACS and patient risk factors is typically non-linear and highly variable across patient lifespan. Here, we aim to uncover deeper insights into the factors that shape ACS outcomes in hospitals across four Arabian Gulf countries. Further, because anemia is one of the most observed comorbidities, we explored its role in the prognosis of most prevalent ACS in-hospital outcomes (mortality, heart failure, and bleeding) in the region. We used a robust multi-algorithm interpretable machine learning (ML) pipeline, and 20 relevant risk factors to fit predictive models to 4,044 patients presenting with ACS between 2012 and 2013. We found that in-hospital heart failure followed by anemia was the most important predictor of mortality. However, anemia was the first most important predictor for both in-hospital heart failure, and bleeding. For all in-hospital outcome, anemia had remarkably non-linear relationships with both ACS outcomes and patients' baseline characteristics. With minimal statistical assumptions, our ML models had reasonable predictive performance (AUCs > 0.75) and substantially outperformed commonly used statistical and risk stratification methods. Moreover, our pipeline was able to elucidate ACS risk of individual patients based on their unique risk factors. Fully interpretable ML approaches are rarely used in clinical settings, particularly in the Middle East, but have the potential to improve clinicians' prognostic efforts and guide policymakers in reducing the health and economic burdens of ACS worldwide.
MeSH term(s)	Acute Coronary Syndrome/mortality ; Acute Coronary Syndrome/therapy ; Aged ; Anemia/mortality ; Anemia/therapy ; Comorbidity ; Female ; Hospital Mortality ; Humans ; Machine Learning ; Male ; Middle Aged ; Middle East/epidemiology ; Models, Cardiovascular ; Patient Admission ; Registries ; Risk Assessment
Language	English
Publishing date	2022-01-24
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0262997
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

To top

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito