A Systematic Review of Machine Learning Models for Predicting Outcomes of Stroke with Structured Data
| Autor: | Vasa Curcin, Iain J. Marshall, Charles D.A. Wolfe, Benjamin Bray, Wenjuan Wang, Anthony Rudd, Yanzhong Wang, Abdel Douiri, Niels Peek, Martin Kiik |
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| Rok vydání: | 2020 |
| Předmět: |
Decision Analysis
Declaration computer.software_genre Vascular Medicine Machine Learning 0302 clinical medicine Mathematical and Statistical Techniques Medicine and Health Sciences 030212 general & internal medicine Stroke Statistical Data Multidisciplinary Applied Mathematics Simulation and Modeling Statistics Research Assessment Prognosis Stroke/diagnosis Medical research Checklist Random forest Systematic review Neurology Physical Sciences Medicine Engineering and Technology Psychology Management Engineering Inclusion (education) Algorithms Research Article Computer and Information Sciences Systematic Reviews Science Cerebrovascular Diseases Decision tree Research and Analysis Methods Machine learning 03 medical and health sciences Artificial Intelligence medicine Humans Statistical Methods Artificial Neural Networks Computational Neuroscience Models Statistical business.industry Decision Trees Conflict of interest Biology and Life Sciences Computational Biology medicine.disease Decision Tree Learning Support vector machine Sample size determination Artificial intelligence business computer 030217 neurology & neurosurgery Mathematics Predictive modelling Forecasting Neuroscience |
| Zdroj: | Wang, W, Kiik, M, Peek, N, Curcin, V, Marshall, I J, Rudd, A G, Wang, Y, Douiri, A, Wolfe, C D & Bray, B 2020, ' A systematic review of machine learning models for predicting outcomes of stroke with structured data ', PLoS ONE, vol. 15, no. 6, pp. e0234722 . https://doi.org/10.1371/journal.pone.0234722 PLoS ONE PLoS ONE, Vol 15, Iss 6, p e0234722 (2020) |
| ISSN: | 1556-5068 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
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