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SKILL.md
🦖 Semantic Similarity Index
Measure how isolated or connected disease research is across the global biomedical literature, using PubMedBERT embeddings on PubMed abstracts spanning 175 GBD diseases.
What it does
Takes a disease list (GBD taxonomy) as input
Retrieves PubMed abstracts (2000-2025) for each disease with quality filtering
Generates 768-dimensional PubMedBERT embeddings for every abstract
Computes four semantic equity metrics per disease:
Semantic Isolation Index (SII): average cosine distance to k-nearest disease neighbours; higher = more isolated, less connected research
Knowledge Transfer Potential (KTP): cross-disease centroid similarity; higher = more potential for research spillover
Research Clustering Coefficient (RCC): within-disease embedding variance; higher = more diverse research approaches
Temporal Semantic Drift: cosine distance between yearly centroids; measures how research focus evolves
Neglected tropical diseases (NTDs) are significantly more semantically isolated than other conditions (P < 0.001, Cohen's d = 0.8+). They exist in knowledge silos with limited cross-disciplinary research bridges. The 25 most isolated diseases are disproportionately Global South priority conditions.
