Improved Interpretability Without Performance Reduction in a Sepsis Prediction Risk Score Article Swipe
YOU?
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· 2025
· Open Access
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· DOI: https://doi.org/10.3390/diagnostics15030307
Objective: Sepsis is a life-threatening response to infection and a major cause of hospital mortality. Machine learning (ML) models have demonstrated better sepsis prediction performance than integer risk scores but are less widely used in clinical settings, in part due to lower interpretability. This study aimed to improve the interpretability of an ML-based model without reducing its performance in non-ICU sepsis prediction. Methods: A logistic regression model was trained to predict sepsis onset and then converted into a more interpretable integer point system, STEWS, using its regression coefficients. We compared STEWS with the logistic regression model using PPV at 90% sensitivity. Results: STEWS was significantly equivalent to logistic regression using the two one-sided tests procedure (0.051 vs. 0.051; p = 0.004). Conclusions: STEWS demonstrated equivalent performance to a comparable logistic regression model for non-ICU sepsis prediction, suggesting that converting ML models into more interpretable forms does not necessarily reduce predictive power.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics15030307
- OA Status
- gold
- References
- 33
- Related Works
- 10
- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4406913905Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/diagnostics15030307Digital Object Identifier
- Title
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Improved Interpretability Without Performance Reduction in a Sepsis Prediction Risk ScoreWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-28Full publication date if available
- Authors
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Adam Kotter, Samir Abdelrahman, Yik-Ki Jacob Wan, Karl Madaras-Kelly, Keaton Morgan, Chin Fung Kelvin Kan, Guilherme Del FiolList of authors in order
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https://doi.org/10.3390/diagnostics15030307Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/diagnostics15030307Direct OA link when available
- Concepts
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Interpretability, Logistic regression, Sepsis, Regression analysis, Computer science, Medicine, Machine learning, Internal medicineTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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