The DOI matches its Crossref record.
1405: ASSESSING CLINICAL USE AND PERFORMANCE OF A MACHINE LEARNING SEPSIS ALERT FOR SEX AND RACIAL BIAS
Abstract
Adams, Roy; Henry, Katharine; Soleimani, Hossein; Rawat, Nishi; Saheed, Mustapha; Chen, Edward; Wu, Albert; Saria, Suchi Author Information
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One of the most widely deployed sepsis early warning systems had a sensitivity of only 33% and a precision of 2.4% (1 in 46 alerts within 24 hours of sepsis onset).
“For example, one of the most widely deployed sepsis early warning systems had a sensitivity of only 33% and a precision of 2.4% (1 in 46 alerts within 24 hours of sepsis onset) 42 .”
From Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing (2022) · cites this paper as [42]
Past deployed systems have reported significantly lower predictive performance on similar hospital populations.
“Past deployed systems have reported significantly lower predictive performance on similar hospital populations 20,42,45 .”
From Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing (2022) · cites this paper as [42]
Citations
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Henry, K. E., Adams, R., Parent, C., Soleimani, H., Sridharan, A., Johnson, L., Hager, D. N., Cosgrove, S. E., Markowski, A., Klein, E. Y., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01895-z · Cited by 144
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Metadata
- Authors
- Adams, R., Henry, K., Soleimani, H., Rawat, N., Saheed, M., Chen, E., Wu, A., & Saria, S.
- Affiliations
- Johns Hopkins University (US)
- University of California, San Francisco (US)
- Venue
- Critical Care Medicine
- Published
- 2021-12-16
- Volume
- 50
- Issue
- 1
- Pages
- 705-705
- ISSN
- 0090-3493 (ISSN-L)
- OpenAlex
- W4200415272
- Cited by
- 5
- References
- 0
- Citations by year
- 2025: 2 · 2024: 2 · 2022: 1
- FWCI
- 0.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Critical Care Medicine (publishedVersion)
- Topics
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Sepsis Diagnosis and Treatment
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- sepsis alerts, machine learning, algorithmic bias, sex bias, racial bias, clinical implementation, sepsis detection, electronic health records
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