ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2022
Online Critical-State Detection of Sepsis Among ICU Patients using Jensen-Shannon Divergence.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Beyond demographic tables: integrating data quality in clinical trial representativeness.Frontiers in digital health · 2026Article
- Soft phenotyping for sepsis via EHR time-aware soft clustering.Journal of biomedical informatics · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis is a severe medical condition caused by a dysregulated host response to infection that has a high incidence and mortality rate. Even with such a high-level occurrence rate, the detection and diagnosis of sepsis continues to pose a challenge. There is a crucial need to accurately forecast the onset of sepsis promptly while also identifying the specific physiologic anomalies that contribute to this prediction in an interpretable fashion. This study proposes a novel approach to quantitatively measure the difference between patients and a reference group using non-parametric probability distribution estimates and highlight when abnormalities emerge using a Jensen-Shannon divergence- based single sample analysis approach. We show that we can quantitatively distinguish between these two groups and offer a measurement of divergence in real time while simultaneously identifying specific physiologic factors contributing to patient outcomes. We demonstrate our approach on a real-world dataset of patients admitted to Atlanta, Georgia's Grady Hospital.
Indexed as
Identifiers
37128380PMC10148343What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.