A systematic review of machine learning models for predicting outcomes of stroke with structured data.

ABSTRACT:

Background and purpose

Machine learning (ML) has attracted much attention with the hope that it could make use of large, routinely collected datasets and deliver accurate personalised prognosis. The aim of this systematic review is to identify and critically appraise the reporting and developing of ML models for predicting outcomes after stroke.

Methods

We searched PubMed and Web of Science from 1990 to March 2019, using previously published search filters for stroke, ML, and prediction models. We focused on structured clinical data, excluding image and text analysis. This review was registered with PROSPERO (CRD42019127154).

Results

Eighteen studies were eligible for inclusion. Most studies reported less than half of the terms in the reporting quality checklist. The

SUBMITTER: Wang W 

PROVIDER: S-EPMC7292406 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature