MACHINE LEARNING CLUSTERING FOR BLOOD PRESSURE VARIABILITY: VALIDATION FROM THE SPRINT TO THE HONG KONG COMMUNITY COHORT
Bibliographic record
Abstract
Objective: Visit-to-visit BPV is associated with risks of cardiovascular diseases. Our aim is to investigate the classification methods of patients with varying levels of BPV using different machine learning algorithms. Design and method: Two sets of visit-to-visit blood pressure (BP) readings were extracted from (i) SPRINT in the United States and (ii) BP cohort in Hong Kong (HK). BPV were defined as the mean absolute residuals of regression trends on BP over time. Patients were clustered into low, medium and high levels of BPV with the traditional quantile clustering and five machine learning algorithms, including K-means clustering, Partitioning Around Medoids (PAM), Spectral clustering, Ward's method and Expectation Maximization. Clustering methods were assessed by the Stability Index, and similarities were assessed by the Davies-Bouldin Index (DBI) and the Silhouette Index. Regression models were fitted to compare the risk of stroke. Results: Results: A total of 8,133 participants were included from SPRINT with the mean BP measurement 14.7 times in 3.28 years of follow-up, and1,094 participants were recruited from HK cohort with the mean BP measurement 165.4 times in 1.37 years. Quantile clustering assigned one-third participants as the high level of BPV, but machine learning methods only assigned 10 to 27%. Quantile clustering is the most stable method (Stability Index: 0.982 in the SPRINT and 0.948 in the HK cohort) but shown to have certain levels of cluster similarities (DBI: 0.752 and 0.764, respectively) (Table 1). Across the machine learning algorithms, K-means clustering is the most stable method (Stability Index: 0.975 and 0.911, respectively) with lowest similarities on cluster classification (DBI: 0.653 and 0.680, respectively). Patients with high level of BPV under the machine learning models showed stronger association with stroke. Conclusions: Among the machine learning algorithms, K-means clustering shows the most stable and reliable results. One-seventh of the population with high level of BPV had elevated risk of stroke compared to conventional BPV classification. Machine learning can be potentially used in the electronic health system for better patient management.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".