Assessment of the timeliness and robustness for predicting adult sepsis
Open accessAbstract
Sepsis is a leading cause of death among inpatients at hospitals. However, with early detection, death rate can drop substantially. In this study, we present the top-performing algorithm for Sepsis II prediction in the DII National Data Science Challenge using the Cerner Health Facts data involving more than 100,000 adult patients. This large sample size allowed us to dissect the predictability by age-groups, race, genders, and care settings and up to 192 hr of sepsis onset. This large data collection also allowed us to conclude that the last six biometric records on average are informative to the prediction of sepsis. We identified biomarkers that are common across the treatment time and novel biomarkers that are uniquely presented for early prediction. The algorithms showed meaningful signals days ahead of sepsis onset, supporting the potential of reducing death rate by focusing on high-risk populations identified from heterogeneous data integration.
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Addressing the robustness of the model to specific minority groups directly relates to the notion of fairness.
“addressing the robustness of the model to specific minority groups directly relates to the notion of fairness23–25.”
From A scoping review of robustness concepts for machine learning in healthcare (2025) · cites this paper as [23]
The Results section evaluates sepsis-model performance across age, gender, and racial groups, including a small Asian subgroup for which performance was slightly worse (page unavailable). That supports the subgroup-robustness part of the claim, but our copy does not explicitly connect this evaluation to fairness. (LLM, conf=0.82)
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Self-citation
- Authors
- Guan, Y., Wang, X., Chen, X., Yi, D., Chen, L., & Jiang, X.
- Affiliations
- University of Michigan (US)
- The University of Texas Health Science Center at Houston (US)
- Corresponding author
- Guan, Yuanfang
- Venue
- iScience
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 24
- Issue
- 2
- Pages
- 102106
- ISSN
- 2589-0042 (ISSN-L)
- PMC
- PMC7895752
- OpenAlex
- W3122820077
- Cited by
- 7
- References
- 25
- Citations by year
- 2025: 3 · 2023: 1 · 2022: 1 · 2021: 2
- FWCI
- 0.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- iScience (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (1452656)
- Topics
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- sepsis prediction, early detection, adult patients, biomarkers, age differences, race and gender differences, care environment, robustness, vulnerable populations
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