Temporal phenotyping and prognostic stratification of patients with sepsis through longitudinal clustering
Bibliographic record
Abstract
Sepsis is a critical medical condition characterized by a highly variable and rapidly evolving clinical course, often necessitating early intervention and tailored treatment plans to improve patient outcomes. Due to its complexity and heterogeneity, understanding the progression of sepsis across different patient populations remains a significant challenge. In this study, we exploit a sophisticated analytical framework based on k-means multivariate longitudinal clustering to capture the diverse trajectories of sepsis. We do so by analyzing multiple clinical parameters tracked over time, providing a nuanced view of disease progression. By incorporating Dynamic Time Warping (DTW) as the distance metric, the proposed method effectively accounts for temporal misalignments and variability in the rate of disease progression, an essential capability given the unpredictable and heterogeneous nature of sepsis. This integration enhances the model's ability to detect distinct temporal patterns and phenotypic subgroups that may remain undetected using conventional analytical approaches. By leveraging sepsis-related electronic health records (EHRs), which provide rich time-series data on laboratory results along with patient demographics and underlying health conditions, the proposed method reveals distinct sepsis phenotypes that reflect variations in disease progression. We perform several experiments varying the number of clusters and clinical variable combinations, evaluating the clustering performances using Silhouette score, Caliski-Harabasz Index, and Davies-Bouldin Index, as reference quality metrics. Our results confirm the prognostic role of the Thrombin-Antigen complex and the Prothrombin Time-International Normalized Ratio for septic patients. Furthermore, to evaluate the relevance of subjects' stratification, the Adjusted Rand Index metric is used to quantify the survival prediction capability of our longitudinal clustering method, considering the 28-day death feature as the target variable. The same metric demonstrates that our proposal outperforms other longitudinal clustering algorithms available in the literature.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".