Machine learning-based prediction of one-year mortality in ischemic stroke patients Article Swipe
Ahmad A. Abujaber
,
Said Yaseen
,
Yahia Imam
,
Abdulqadir J. Nashwan
,
Naveed Akhtar
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/oons/kvae011
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/oons/kvae011
Background: Accurate prediction of mortality following an ischemic stroke is essential for tailoring personalized treatment strategies. This study evaluates the effectiveness of machine learning models in predicting one-year mortality after an ischemic stroke. Methods: Five machine learning models were trained using data from a national stroke registry, with logistic regression demonstrating the highest performance. The SHapley Additive exPlanations (SHAP) analysis explained the model’s outcomes and defined the influential predictive factors. Results: Analyzing 8183 ischemic stroke patients, logistic regression achieved 83% accuracy, 0.89 AUC, and an F1 score of 0.83. Significant predictors included stroke severity, pre-stroke functional status, age, hospital-acquired pneumonia, ischemic stroke subtype, tobacco use, and co-existing diabetes mellitus (DM). Discussion: The model highlights the importance of predicting mortality in enhancing personalized stroke care. Apart from pneumonia, all predictors can serve the early prediction of mortality risk which supports the initiation of early preventive measures and in setting realistic expectations of disease outcomes for all stakeholders. The identified tobacco paradox warrants further investigation. Conclusion: This study offers a promising tool for early prediction of stroke mortality and for advancing personalized stroke care. It emphasizes the need for prospective studies to validate these findings in diverse clinical settings.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/oons/kvae011
- https://academic.oup.com/oons/advance-article-pdf/doi/10.1093/oons/kvae011/60678054/kvae011.pdf
- OA Status
- diamond
- Cited By
- 2
- References
- 72
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404350655
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404350655Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/oons/kvae011Digital Object Identifier
- Title
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Machine learning-based prediction of one-year mortality in ischemic stroke patientsWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Ahmad A. Abujaber, Said Yaseen, Yahia Imam, Abdulqadir J. Nashwan, Naveed AkhtarList of authors in order
- Landing page
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https://doi.org/10.1093/oons/kvae011Publisher landing page
- PDF URL
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https://academic.oup.com/oons/advance-article-pdf/doi/10.1093/oons/kvae011/60678054/kvae011.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://academic.oup.com/oons/advance-article-pdf/doi/10.1093/oons/kvae011/60678054/kvae011.pdfDirect OA link when available
- Concepts
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Ischemic stroke, Stroke (engine), Medicine, Artificial intelligence, Cardiology, Internal medicine, Machine learning, Physical medicine and rehabilitation, Computer science, Ischemia, Engineering, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
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72Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Oxford Open Neuroscience |
| primary_location.landing_page_url | https://doi.org/10.1093/oons/kvae011 |
| publication_date | 2024-01-01 |
| publication_year | 2024 |
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