systematic review | Digital Health

Reps JM, Kim C, Williams RD, Markus AF, Yang C, Salles TD, Falconer T, Jonnagaddala J, Williams A, Fernandez-Bertolin S, DuVall SL, Kostka K, Rao G, Shoaibi A, Ostropolets A, Spotnitz ME, Zhang L, Casajust P, Steyerberg E, Nyberg F, Kaas-Hansen BS, Choi YH, Morales D, Liaw S-T, Abrahao MTF, Areia C, Matheny ME, Aragon M, Park RW, Hripcsak G, Reich CG, Suchard MA, You SC, Ryan PB, Prieto-Alhambra D, Rijnbeek PR. Can we trust the prediction model? Demonstrating the importance of external validation by investigating the COVID-19 Vulnerability (C-19) Index across an international network of observational healthcare datasets. medRxiv. 2020:2020.06.15.20130328.

Background: SARS-CoV-2 is straining healthcare systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate between patients requiring hospitalization and those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia

Byambasuren O, Dobler CC, Bell K, Rojas DP, Clark J, McLaws M-L, Glasziou P. Comparison of seroprevalence of SARS-CoV-2 infections with cumulative and imputed COVID-19 cases: systematic review. medRxiv. 2020:2020.07.13.20153163. preprint

Background. Accurate seroprevalence estimates of SARS-CoV-2 in different populations could clarify the extent to which current testing strategies are identifying all active infection, and hence the true magnitude and spread of the infection. Our primary objective was to identify valid seroprevalence studies of SARS-CoV-2 infection and compare their estimates with the reported and imputed COVID-19

Byambasuren O, Cardona M, Bell K, Clark J, McLaws M-L, Glasziou P. Estimating the extent of asymptomatic COVID-19 and its potential for community transmission: systematic review and meta-analysis. medRxiv. 2020:2020.05.10.20097543.

Background: The prevalence of true asymptomatic COVID-19 cases is critical to policy makers considering the effectiveness of mitigation measures against the SARS-CoV-2 pandemic. We aimed to synthesize all available research on the asymptomatic rates and transmission rates where possible. Methods: We searched PubMed, Embase, Cochrane COVID-19 trials, and Europe PMC (which covers pre-print platforms such

Bakhit M, Krzyzaniak N, Scott AM, Clark J, Glasziou P, Del Mar C. Downsides of face masks and possible mitigation strategies: a systematic review and meta-analysis. medRxiv. 2020:2020.06.16.20133207.

Objective To identify, appraise, and synthesise studies evaluating the downsides of wearing facemasks in any setting. We also discuss potential strategies to mitigate these downsides. Methods PubMed, Embase, CENTRAL, EuropePMC were searched (inception-18/5/2020), and clinical registries were searched via CENTRAL. We also did forward-backward citation search of the included studies. We included randomised controlled trials

Scott AM, Clark J, Del Mar C, Glasziou P, Increased fluid intake to prevent urinary tract infections: systematic review and meta-analysis. British Journal of General Practice, 70(692), pp.e200-e207.

Background Approximately 15% of community-prescribed antibiotics are used in treating urinary tract infections (UTIs). Increase in antibiotic resistance necessitates considering alternatives. Aim To assess the impact of increased fluid intake in individuals at risk for UTIs, for impact on UTI recurrence (primary outcome), antimicrobial use, and UTI symptoms (secondary outcomes). Design and setting A systematic

Clark, J., Glasziou, P., Del Mar, C., Bannach-Brown, A., Stehlik, P. and Scott, A.M., 2020. A full systematic review was completed in 2 weeks using automation tools: a case study. Journal of Clinical Epidemiology, 121, pp.81-90.

Background and Objectives: Systematic reviews (SRs) are time and resource intensive, requiring approximately 1 year from protocol registration to submission for publication. Our aim was to describe the process, facilitators, and barriers to completing the first 2-week full SR. Study Design and Setting: We systematically reviewed evidence of the impact of increased fluid intake, on

O’Connor AM, Tsafnat G, Gilbert SB, Thayer KA, Shemilt I, Thomas J, Glasziou P, Wolfe MS. Still moving toward automation of the systematic review process: a summary of discussions at the third meeting of the International Collaboration for Automation of Systematic Reviews (ICASR). Systematic Reviews. 2019; 8(1):57.

The third meeting of the International Collaboration for Automation of Systematic Reviews (ICASR) was held 17–18 October 2017 in London, England. ICASR is an interdisciplinary group whose goal is to maximize the use of technology for conducting rapid, accurate, and efficient systematic reviews of scientific evidence. The group seeks to facilitate the development and widespread

Beller E, Clark J, Tsafnat G, Adams C, Diehl H, Lund H, Ouzzani M, Thayer K, Thomas J, Turner T, Xia J, Robinson K, Glasziou P, founding members of the Ig. Making progress with the automation of systematic reviews: principles of the International Collaboration for the Automation of Systematic Reviews (ICASR). Systematic reviews. Syst Rev 7, 77 (2018)

Systematic reviews (SR) are vital to health care, but have become complicated and time-consuming, due to the rapid expansion of evidence to be synthesised. Fortunately, many tasks of systematic reviews have the potential to be automated or may be assisted by automation. Recent advances in natural language processing, text mining and machine learning have produced

Hassanzadeh H, Nguyen A, Verspoor K. Quantifying semantic similarity of clinical evidence in the biomedical literature to facilitate related evidence synthesis. Journal of Biomedical Informatics. 2019;100:103321.

Objective: Published clinical trials and high quality peer reviewed medical publications are considered as the main sources of evidence used for synthesizing systematic reviews or practicing Evidence Based Medicine (EBM). Finding all relevant published evidence for a particular medical case is a time and labour intensive task, given the breadth of the biomedical literature. Automatic

Adopting machine learning in health research

Traditional methods are often ill-suited to the rapidly evolving world of health care research, characterised by data volume, complexity and pace. Machine learning offers an opportunity to address challenges in all facets of health research but is often subject to bias which limits its use. A team of digital health researchers, led by Annette O’Connor