Generating a knowledge graph to understand the mechanistic relationships between multimorbid diabetes, hypertension and kidney diseases
Bibliographic record
Abstract
Multimorbidity, defined as the coexistence of two or more chronic diseases, poses significant challenges to patient care. Understanding the mechanistic relationships between diseases is crucial for improving patient outcomes. This study explores the use of a knowledge graph to identify shared risk factors, common biological pathways, and key disease interactions contributing to the development and progression of these multimorbid conditions. A total of 8,006 semantic triples (subject-predicate-object) were identified, serving as the foundation for constructing our knowledge graph using Neo4j. Our graph comprised 4,391 unique nodes and 10,083 edges. Queries conducted on the knowledge graph identified shared risk factors among diabetes, hypertension, and kidney disease, highlighting the interconnected nature of multimorbid conditions. Additionally, our graph provided valuable insights into drug-disease interactions, demonstrating that while a drug may be beneficial for a specific condition, it could also exacerbate the other condition in a multimorbid setting.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".