Author ORCID Identifier

https://orcid.org/0000-0002-1767-4744

Document Type

Article

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

1.2 COMPUTER AND INFORMATION SCIENCE, Public and environmental health, Epidemiology

Publication Details

Frontiers in Neuroinformatics

Part of the research topic: Insights in Neuroinformatics: 2021

https://www.frontiersin.org/articles/10.3389/fninf.2022.883762/full

Abstract

Predicting an individual's risk of primary stroke is an important tool that can help to lower the burden of stroke for both the individual and society. There are a number of risk models and risk scores in existence but no review or classification designed to help the reader better understand how models differ and the reasoning behind these differences. In this paper we review the existing literature on primary stroke risk prediction models. From our literature review we identify key similarities and differences in the existing models. We find that models can differ in a number of ways, including the event type, the type of analysis, the model type and the time horizon. Based on these similarities and differences we have created a set of questions and a system to help answer those questions that modelers and readers alike can use to help classify and better understand the existing models as well as help to make necessary decisions when creating a new model.

DOI

https://doi.org/10.3389/fninf.2022.883762

Funder

EU's Horizon 2020; ADAPT Centre for Digital Content Technology

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