extracting-clinical-entities
本地化NER、返回置信度与偏移，便于安全处理与下游分析。
需下载模型并处理敏感医疗文本，注意数据保护与模型更新来源。
使用OpenMed的analyze_text在本地运行临床与生物医学命名实体识别。
在本地对临床文本做命名实体识别（NER），把疾病、药物、解剖部位、基因等抽出来并给出字符偏移和置信度。适合要从病历里提取结构化实体用于统计、导出为 JSON/CSV/HTML 的场景，也可以按置信度过滤或分组实体。和先做去标识化配合使用，输出精确的 span 方便后续审计和处理。
▸ 展开 SKILL.md 英文原文
Run clinical and biomedical named-entity recognition on medical text with OpenMed's analyze_text. Use when the user wants to extract diseases, drugs, anatomy, genes, or other biomedical entities from notes; needs NER output as dict/json/html/csv; wants to filter by confidence, group entities, toggle sentence detection, or save spans to JSONL; or wants the openmed analyze CLI. Pairs with loading-openmed-models and choosing-openmed-models, and runs after deidentifying-clinical-text in a privacy-first pipeline.
帮我安装这个 skill：https://raw.githubusercontent.com/maziyarpanahi/openmed/master/skills/extracting-clinical-entities/SKILL.mdcurl -fsSL "https://raw.githubusercontent.com/maziyarpanahi/openmed/master/skills/extracting-clinical-entities/SKILL.md"# Extracting Clinical Entities `openmed.analyze_text` runs a token-classification model over medical text and returns structured entities with character offsets and confidence scores. It runs **on-device** after a one-time model download. ## When to use - Pull diseases, medications, anatomy, genes, proteins, etc. out of clinical text. - You need exact **character spans** (start/end) plus **confidence** per entity. - You want output as objects, JSON, an HTML highlight view, or CSV. - You are building the "extract entities" stage of a clinical NLP pipeline. To choose a model, see `choosing-openmed-models`. To load it once and reuse it, see `loading-openmed-models`. **In a PHI workflow, de-