scholarly journals The impact of 2019 novel coronavirus on heart injury: A Systematic review and Meta-analysis

2020 ◽  
Vol 63 (4) ◽  
pp. 518-524 ◽  
Author(s):  
Jing-Wei Li ◽  
Tian-Wen Han ◽  
Mark Woodward ◽  
Craig S. Anderson ◽  
Hao Zhou ◽  
...  
Keyword(s):  
Systematic Review ◽  
Meta Analysis ◽  
Heart Injury ◽  
Novel Coronavirus ◽  
The Impact

scholarly journals The Role of Machine Learning Techniques to Tackle COVID-19 Crisis: A Systematic Review.

2020 ◽  
Author(s):  
Hafsa Bareen Syeda ◽  
Mahanazuddin Syed ◽  
Kevin Wayne Sexton ◽  
Shorabuddin Syed ◽  
Salma Begum ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Disease Progression ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Data Types ◽  
Detection And Diagnosis ◽  
Novel Coronavirus ◽  
The Impact

Background: The novel coronavirus responsible for COVID-19 has caused havoc with patients presenting a spectrum of complications forcing the healthcare experts around the globe to explore new technological solutions, and treatment plans. Machine learning (ML) based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize them in response to the challenges posed by the COVID-19 pandemic. Objective: The objective of this study is to conduct a systematic literature review on the role of ML as a comprehensive and decisive technology to fight the COVID-19 crisis in the arena of epidemiology, diagnosis, and disease progression. Methods: A systematic search in PubMed, Web of Science, and CINAHL databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to identify all potentially relevant studies published and made available between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and ML. A total of 128 qualified articles were reviewed and analyzed based on the study objectives. Results: The 128 publications selected were classified into three themes based on ML applications employed to combat the COVID-19 crisis: Computational Epidemiology (CE), Early Detection and Diagnosis (EDD), and Disease Progression (DP). Of the 128 studies, 70 focused on predicting the outbreak, the impact of containment policies, and potential drug discoveries, which were grouped into the CE theme. For the EDD, we grouped forty studies that applied ML techniques to detect the presence of COVID-19 using the patient's radiological images or lab results. Eighteen publications that focused on predicting the disease progression, outcomes (recovery and mortality), Length of Stay (LOS), and number of Intensive Care Unit (ICU) days for COVID-19 positive patients were classified under the DP theme. Conclusions: In this systematic review, we assembled the current COVID-19 literature that utilized ML methods to provide insights into the COVID-19 themes, highlighting the important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.

scholarly journals The Role of Machine Learning Techniques to Tackle COVID-19 Crisis: A Systematic Review. (Preprint)

2020 ◽  
Author(s):  
Hafsa Bareen Syeda ◽  
Mahanazuddin Syed ◽  
Kevin Wayne Sexton ◽  
Shorabuddin Syed ◽  
Salma Begum ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Disease Progression ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Data Types ◽  
Detection And Diagnosis ◽  
Novel Coronavirus ◽  
The Impact

BACKGROUND The novel coronavirus responsible for COVID-19 has caused havoc with patients presenting a spectrum of complications forcing the healthcare experts around the globe to explore new technological solutions, and treatment plans. Machine learning (ML) based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize them in response to the challenges posed by the COVID-19 pandemic. OBJECTIVE The objective of this study is to conduct a systematic literature review on the role of ML as a comprehensive and decisive technology to fight the COVID-19 crisis in the arena of epidemiology, diagnosis, and disease progression. METHODS A systematic search in PubMed, Web of Science, and CINAHL databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to identify all potentially relevant studies published and made available between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and ML. A total of 128 qualified articles were reviewed and analyzed based on the study objectives. RESULTS The 128 publications selected were classified into three themes based on ML applications employed to combat the COVID-19 crisis: Computational Epidemiology (CE), Early Detection and Diagnosis (EDD), and Disease Progression (DP). Of the 128 studies, 70 focused on predicting the outbreak, the impact of containment policies, and potential drug discoveries, which were grouped into the CE theme. For the EDD, we grouped forty studies that applied ML techniques to detect the presence of COVID-19 using the patient's radiological images or lab results. Eighteen publications that focused on predicting the disease progression, outcomes (recovery and mortality), Length of Stay (LOS), and number of Intensive Care Unit (ICU) days for COVID-19 positive patients were classified under the DP theme. CONCLUSIONS In this systematic review, we assembled the current COVID-19 literature that utilized ML methods to provide insights into the COVID-19 themes, highlighting the important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.

scholarly journals Misuse of medication in adult substance misuse services: a systematic review protocol

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e047283
Author(s):  
Rosalind Gittins ◽  
Louise Missen ◽  
Ian Maidment
Keyword(s):  
Systematic Review ◽  
Substance Misuse ◽  
Data Extraction ◽  
Meta Analysis ◽  
Grey Literature ◽  
Opioid Analgesics ◽  
Physical And Mental Health ◽  
Qualitative Synthesis ◽  
Treatment Services ◽  
The Impact

IntroductionThere is a growing concern about the misuse of over the counter (OTC) and prescription only medication (POM) because of the impact on physical and mental health, drug interactions, overdoses and drug-related deaths. These medicines include opioid analgesics, anxiolytics such as pregabalin and diazepam and antidepressants. This protocol outlines how a systematic review will be undertaken (during June 2021), which aims to examine the literature on the pattern of OTC and POM misuse among adults who are accessing substance misuse treatment services. It will include the types of medication being taken, prevalence and demographic characteristics of people who access treatment services.Methods and analysisAn electronic search will be conducted on the Cochrane, OVID Medline, Pubmed, Scopus and Web of Science databases as well as grey literature. Two independent reviewers will conduct the initial title and abstract screenings, using predetermined criteria for inclusion and exclusion. If selected for inclusion, full-text data extraction will be conducted using a pilot-tested data extraction form. A third reviewer will resolve disagreements if consensus cannot be reached. Quality and risk of bias assessment will be conducted for all included studies. A qualitative synthesis and summary of the data will be provided. If possible, a meta-analysis with heterogeneity calculation will be conducted; otherwise, Synthesis Without Meta-analysis will be undertaken for quantitative data. The reporting of this protocol follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses.Ethics and disseminationEthical approval is not required. Findings will be peer reviewed, published and shared verbally, electronically and in print, with interested clinicians and policymakers.PROSPERO registration numberCRD42020135216.

scholarly journals P20-8 The impact of valproic acid on overall survival in patients with glioblastoma: A systematic review and meta-analysis

2021 ◽  
Vol 32 ◽  
pp. S340
Author(s):  
Charlotte A. Jonatan ◽  
Elizabeth Marcella ◽  
Jeannette Tandiono ◽  
Sharon Chen ◽  
Felix Wijovi ◽  
...  
Keyword(s):  
Systematic Review ◽  
Valproic Acid ◽  
Overall Survival ◽  
Meta Analysis ◽  
The Impact
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