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Soft phenotyping for sepsis via EHR time-aware soft clustering.

Publication ,  Journal Article
Jiang, S; Gai, X; Treggiari, MM; Stead, WW; Zhao, Y; Page, CD; Zhang, AR
Published in: J Biomed Inform
April 2024

OBJECTIVE: Sepsis is one of the most serious hospital conditions associated with high mortality. Sepsis is the result of a dysregulated immune response to infection that can lead to multiple organ dysfunction and death. Due to the wide variability in the causes of sepsis, clinical presentation, and the recovery trajectories, identifying sepsis sub-phenotypes is crucial to advance our understanding of sepsis characterization, to choose targeted treatments and optimal timing of interventions, and to improve prognostication. Prior studies have described different sub-phenotypes of sepsis using organ-specific characteristics. These studies applied clustering algorithms to electronic health records (EHRs) to identify disease sub-phenotypes. However, prior approaches did not capture temporal information and made uncertain assumptions about the relationships among the sub-phenotypes for clustering procedures. METHODS: We developed a time-aware soft clustering algorithm guided by clinical variables to identify sepsis sub-phenotypes using data available in the EHR. RESULTS: We identified six novel sepsis hybrid sub-phenotypes and evaluated them for medical plausibility. In addition, we built an early-warning sepsis prediction model using logistic regression. CONCLUSION: Our results suggest that these novel sepsis hybrid sub-phenotypes are promising to provide more accurate information on sepsis-related organ dysfunction and sepsis recovery trajectories which can be important to inform management decisions and sepsis prognosis.

Duke Scholars

Published In

J Biomed Inform

DOI

EISSN

1532-0480

Publication Date

April 2024

Volume

152

Start / End Page

104615

Location

United States

Related Subject Headings

  • Sepsis
  • Phenotype
  • Medical Informatics
  • Humans
  • Electronic Health Records
  • Cluster Analysis
  • Biomedical Engineering
  • Algorithms
  • 4601 Applied computing
  • 4203 Health services and systems
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Jiang, S., Gai, X., Treggiari, M. M., Stead, W. W., Zhao, Y., Page, C. D., & Zhang, A. R. (2024). Soft phenotyping for sepsis via EHR time-aware soft clustering. J Biomed Inform, 152, 104615. https://doi.org/10.1016/j.jbi.2024.104615
Jiang, Shiyi, Xin Gai, Miriam M. Treggiari, William W. Stead, Yuankang Zhao, C David Page, and Anru R. Zhang. “Soft phenotyping for sepsis via EHR time-aware soft clustering.J Biomed Inform 152 (April 2024): 104615. https://doi.org/10.1016/j.jbi.2024.104615.
Jiang S, Gai X, Treggiari MM, Stead WW, Zhao Y, Page CD, et al. Soft phenotyping for sepsis via EHR time-aware soft clustering. J Biomed Inform. 2024 Apr;152:104615.
Jiang, Shiyi, et al. “Soft phenotyping for sepsis via EHR time-aware soft clustering.J Biomed Inform, vol. 152, Apr. 2024, p. 104615. Pubmed, doi:10.1016/j.jbi.2024.104615.
Jiang S, Gai X, Treggiari MM, Stead WW, Zhao Y, Page CD, Zhang AR. Soft phenotyping for sepsis via EHR time-aware soft clustering. J Biomed Inform. 2024 Apr;152:104615.
Journal cover image

Published In

J Biomed Inform

DOI

EISSN

1532-0480

Publication Date

April 2024

Volume

152

Start / End Page

104615

Location

United States

Related Subject Headings

  • Sepsis
  • Phenotype
  • Medical Informatics
  • Humans
  • Electronic Health Records
  • Cluster Analysis
  • Biomedical Engineering
  • Algorithms
  • 4601 Applied computing
  • 4203 Health services and systems