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Dionisio Acosta

Researcher at University College London

Publications -  24
Citations -  874

Dionisio Acosta is an academic researcher from University College London. The author has contributed to research in topics: Clinical decision support system & Decision support system. The author has an hindex of 11, co-authored 24 publications receiving 763 citations. Previous affiliations of Dionisio Acosta include Autonomous University of Barcelona & University of Sussex.

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Cancer Multidisciplinary Team Meetings: Evidence, Challenges, and the Role of Clinical Decision Support Technology

TL;DR: A targeted summary of the available evidence on the impact of cancer MDT meetings is presented, the reported challenges are discussed, and the role that a CDS technology could play in addressing some of these challenges are explored.
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A Multi-Centre, Web-Accessible and Quality Control-Checked Database of in vivo MR Spectra of Brain Tumour Patients

TL;DR: The validated-DB complies with ethics regulations and represents the population studied and is accessible by neuroradiologists willing to use information provided by MRS to help in the non-invasive diagnosis of brain tumours.
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The relationship between sleep duration, cognition, and dementia: A Mendelian randomization study

TL;DR: In this paper, the authors conducted the first Mendelian randomization (MR) study with 77 single-nucleotide polymorphisms (SNPs) for sleep duration using individual-participant data from the UK Biobank cohort and summary statistics from the International Genomics of Alzheimer's Project (N cases/controls) to investigate the potential impact of sleep duration on cognitive outcomes.
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Using computerised decision support to improve compliance of cancer multidisciplinary meetings with evidence-based guidance

TL;DR: An interactive clinical decision support system called MATE (Multidisciplinary meeting Assistant and Treatment sElector) is developed to facilitate explicit evidence-based decision making in the breast MDMs to enhance the conduct of MDMs in a way that is acceptable to and valued by the clinical team.