Abstract
Background: Machine learning (ML) attracts many attentions 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 and excluded image and text analysis. Findings: Eighteen studies were eligible for inclusion. Most studies reported less than half of the terms in the reporting quality checklist. The most frequently predicted stroke outcomes were mortality (7 studies) and functional independence (5 studies). The most commonly used ML methods were random forests (9 studies), support vector machines (8 studies), decision trees (6 studies), and neural networks (6 studies). The median sample size was 475 (range 70-3184), with a median of 22 predictors (range 4-152) considered. All studies evaluated discrimination with thirteen studies using area under the ROC curve whilst calibration was assessed in three studies. Two studies performed external validation. None of the studies described the final model sufficiently well to reproduce it. Interpretation: Interest in using ML for predicting stroke outcomes is increasing. However, few met basic reporting standards for clinical prediction tools. None of them made their models available in a way which could be used or evaluated. Major improvements in ML study conduct and reporting are needed before meaningfully considered for practice. Funding Statement: CDW, NP, VC, AGR, and WW acknowledge the financial support from the Health Foundation. CDW, AD, VC, and YW acknowledge support from the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) based at Guy’s and St Thomas’ National Health Service (NHS) Foundation Trust and King’s College London, and the NIHR Collaboration for Leadership in Applied Health Research and Care (ARC) South London at King’s College Hospital NHS Foundation Trust. NP acknowledges support from the NIHR Manchester BRC. IJM is funded by the Medical Research Council (MRC), through its Skills Development Fellowship program, fellowship MR/N015185/1. Declaration of Interests: The authors stated: No conflict of interest to declare. Ethics Approval Statement: This review was registered with PROSPERO (CRD42019127154).
Highlights
Stroke is the second leading cause of mortality and disability adjusted life years in the world [1,2]
Major improvements in Machine learning (ML) study conduct and reporting are needed before it can meaningfully be considered for practice
The complexity of a condition such as stroke potentially lends itself well to the use of ML methods which are able to incorporate a large variety of variables and observations into one predictive framework without the need for preprogrammed rules
Summary
Stroke is the second leading cause of mortality and disability adjusted life years in the world [1,2]. Both the outcomes and presentation of stroke can be extremely varied and timely assessment is essential for optimal management. There has been increasing interest in the use of ML to predict stroke outcomes, with the hope that such methods could make use of large, routinely collected datasets and deliver accurate personalised prognoses. The goal of the review was to identify gaps in the literature, critically appraise the reporting and methods of the algorithms and provide the foundation for a wider research program focused on developing novel machine learning based predictive algorithms in stroke care. The aim of this systematic review is to identify and critically appraise the reporting and developing of ML models for predicting outcomes after stroke
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
