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. 2020 Jun 12;15(6):e0234722.
doi: 10.1371/journal.pone.0234722. eCollection 2020.

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

Wenjuan Wang  1 , Martin Kiik  2 , Niels Peek  3   4 , Vasa Curcin  1   5   6 , Iain J Marshall  1 , Anthony G Rudd  1 , Yanzhong Wang  1   5   6 , Abdel Douiri  1   5   6 , Charles D Wolfe  1   5   6 , Benjamin Bray  1
Affiliations

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

Wenjuan Wang et al. PLoS One. .

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 most frequently predicted stroke outcomes were mortality (7 studies) and functional outcome (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 using area under the ROC curve whilst calibration was assessed in three. Two studies performed external validation. None described the final model sufficiently well to reproduce it.

Conclusions: The use of ML for predicting stroke outcomes is increasing. However, few met basic reporting standards for clinical prediction tools and none made their models available in a way which could be used or evaluated. Major improvements in ML study conduct and reporting are needed before it can meaningfully be considered for practice.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. PRISMA flowchart.
Fig 2
Fig 2. Number of papers published according to the algorithms used (top) and outcomes (bottom) predicted at each year.
Fig 3
Fig 3
Number of terms reported in each study (top) and number of studies reported for each assessment term (bottom). * indicates criteria adjusted for ML models.
Fig 4
Fig 4. Boxplots showing the distribution of sample size and feature size according to algorithms used (top) and outcomes predicted (bottom).
See this image and copyright information in PMC

References

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