期刊
SCIENTIFIC REPORTS
卷 11, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41598-021-89434-7
关键词
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资金
- Spanish Ministry of Science and Innovation [SAF201784267-R]
- Xunta de Galicia (Axencia Galega de Innovacion (GAIN)) [IN607A2018/3]
- Instituto de Salud Carlos III (ISCIII) [PI17/00540, PI17/01103]
- Spanish Research Network on Cerebrovascular Diseases RETICS-INVICTUS PLUS [RD16/0019]
- European Union FEDER program
- Miguel Servet Program (Instituto de Salud Carlos III) [CPII17/00027, CPII19/00020]
- General Directorate of Culture, Education and University Management of Xunta de Galicia [ED431G/01, 252 ED431D 2017/16]
- Galician Network for Colorectal Cancer Research [ED431D 2017/23]
- Competitive Reference Groups [ED431C 2018/49]
- Spanish Ministry of Economy and Competitiveness via unique installation BIOCAI [UNLC08-1E-002, UNLC13-13-3503]
- European Regional Development Funds (FEDER)
The study focused on utilizing machine learning techniques to predict mortality and morbidity of stroke patients by examining clinical, biochemical, and neuroimaging factors. A predictive model was generated for long-term outcome prediction, specifically for patients with ischemic stroke and intracerebral hemorrhage.
We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3-months after admission. The dataset consisted of patients with ischemic stroke (IS) and non-traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. We identified the main variables for machine learning Random Forest (RF), generating a predictive model that can estimate patient mortality/morbidity according to the following groups: (1) IS+ICH, (2) IS, and (3) ICH. A total of 6022 patients were included: 4922 (mean age 71.9 +/- 13.8 years) with IS and 1100 (mean age 73.3 +/- 13.1 years) with ICH. NIHSS at 24, 48 h and axillary temperature at admission were the most important variables to consider for evolution of patients at 3-months. IS+ICH group was the most stable for mortality prediction [0.904 +/- 0.025 of area under the receiver operating characteristics curve (AUC)]. IS group presented similar results, although variability between experiments was slightly higher (0.909 +/- 0.032 of AUC). ICH group was the one in which RF had more problems to make adequate predictions (0.9837 vs. 0.7104 of AUC). There were no major differences between IS and IS+ICH groups according to morbidity prediction (0.738 and 0.755 of AUC) but, after checking normality with a Shapiro Wilk test with the null hypothesis that the data follow a normal distribution, it was rejected with W=0.93546 (p-value<2.2e-16). Conditions required for a parametric test do not hold, and we performed a paired Wilcoxon Test assuming the null hypothesis that all the groups have the same performance. The null hypothesis was rejected with a value<2.2e-16, so there are statistical differences between IS and ICH groups. In conclusion, machine learning algorithms RF can be effectively used in stroke patients for long-term outcome prediction of mortality and morbidity.
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