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数据与复现
CL
claw-semantic-sim
维护者 FreedomIntelligence · 最近更新 2026年4月1日
Semantic Similarity Index for disease research literature using PubMedBERT embeddings.
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/claw-semantic-sim
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- Measure how isolated 或 connected disease research is across global biomedical literature，，使用 PubMedBERT embeddings on PubMed abstracts spanning 175 GBD diseases。
- 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 ，用于 research spillover。
- Research 聚类 Coefficient (RCC)：within-disease embedding variance；higher = more diverse research approaches。
- Temporal Semantic Drift：cosine distance between yearly centroids；measures how research focus evolves。
原始文档
SKILL.md 摘录
Why this exists
If you ask ChatGPT to "measure research neglect for diseases," it will:
- Not know which embedding model to use for biomedical text
- Hallucinate metrics that sound plausible but have no methodological grounding
- Skip quality filtering (year coverage, abstract coverage, minimum papers)
- Not handle MPS acceleration or checkpointed batch processing
- Produce a single scatter plot with no disease classification
This skill encodes the correct methodological decisions:
- Uses PubMedBERT (the gold-standard biomedical language model)
- Fetches from PubMed with exponential backoff and NCBI rate limiting
- Quality filters: year coverage >= 70%, abstract coverage >= 95%, minimum 50 papers
- Batch embedding with Apple MPS acceleration and CPU fallback
- Checkpointed processing (resume after interruption)
- HDF5 storage with gzip compression and SHA-256 checksums
- Classification against WHO NTD list and Global South priority diseases
- Statistical significance testing (Welch's t-test, Cohen's d)
Key Finding
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.
Demo (works out of the box)
The demo uses pre-computed embeddings and metrics for 175 GBD diseases and generates the full 4-panel figure instantly.
适用场景
- Use claw-semantic-sim in 科研工作流 aligned ，支持 this subject area。
- Follow upstream documentation ，用于 full working procedure。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
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