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yuhangjiang22
GitHub 제작자 프로필

yuhangjiang22

2개 GitHub 저장소에서 수집된 35개 skills를 저장소 단위로 보여줍니다.

수집된 skills
35
저장소
2
업데이트
2026-07-21
저장소 탐색

저장소와 대표 skills

chart-review-acts
가정의학과 의사

ACTS Alzheimer's/dementia phenotyping — extract from a patient's clinical notes: impaired cognition (impaired_cognition), the documented APOE genotype (apoe_genotype → ε2/ε3/ε4 allele flags computed), postmenopausal status + last menstrual period, documented cognitive / depression / neuropsychiatric scale scores (MoCA, MMSE, CDR, Hachinski, Mattis DRS, TICS, GDS, Cornell, NPI, Global Deterioration stage), education years, and smoking status. Evidence-cited. Triggers on: impaired cognition, MCI, dementia, APOE, ε2/ε3/ε4, postmenopause, LMP, MoCA, MMSE, CDR, Hachinski, NPI, GDS, smoking, ACTS.

2026-07-21
chart-review-bso-ad-ner
데이터 과학자

BSO-AD (Biological / Social / Other determinants of Alzheimer's Disease) NER scope skill. Activates when extracting entities under the BSO-AD ontology — 9 entity-type subtrees covering Demographic, Behavior / Lifestyle, Economic Stability, Education / Literacy, Food, Health Care, Neighborhood, Social and Community Context, and Dementia. Use this skill in combination with the universal chart-review-ner skill when the task_id is "bso-ad-ner" (or any task with task_kind=ner that pins the BSO-AD ontology).

2026-07-20
chart-review-psma-context
방사선 전문의

PSMA PET/CT clinical-context chart review. Activate when assembling the prostate-cancer context a radiologist needs to interpret a PSMA PET/CT — Grade Group, PSA + trend, prior metastatic sites, treatment history (prostatectomy, radiation, ADT, ARPI, chemo, radioligand), the scan indication, and prior-imaging status — answering the PC0–PC3 chart-review questions per patient from pathology, oncology, urology, radiation-oncology, and prior imaging notes plus structured EHR data.

2026-07-20
chart-review-asthma-adherence
의무 기록 전문가

Asthma adherence task. Activates when extracting structured guideline-concordance signals from an asthma patient's chart — tier 0 (eligibility), tier 1 (control assessment: ACT score, exacerbation history, controller use), tier 2 (management: step therapy, spirometry follow-up, written action plan). Use this skill when task_kind=adherence and the task_id is asthma-adherence. The pipeline-extract-adherence package reads the references/questions/*.yaml + references/rules/*.yaml in this bundle to build the tier prompts and concordance verdicts.

2026-07-16
chart-review-cp-depression
가정의학과 의사

Identify evidence of depression from a patient's post-index clinical notes: explicit diagnosis, depressive symptoms, antidepressant use, psychiatry referral, and PHQ-9 severity. Tier and final Depression/No Depression decision are computed. Evidence-cited. Triggers on: depression, PHQ-9, depressive disorder, MDD, antidepressant.

2026-07-13
chart-review-rucam
기타 의사

Score RUCAM (Roussel Uclaf Causality Assessment Method) for drug-induced liver injury (DILI) from EHR structured data + clinical notes. Triggers on: RUCAM, DILI causality, drug-induced liver injury, hepatotoxicity, R ratio, time to onset, rechallenge, liver injury scoring.

2026-07-13
bso-ad
기타 생물 과학자

Extracts named entities from a biomedical / clinical text (PubMed abstract, EHR note, etc.) and normalizes each one to a concept_name from the BSO-AD ontology. Uses MCP tools on the `ner_mcp` server to enumerate the supported entity types, fetch the concept subtree for each type, and map (entity_type, candidate_label) tuples to canonical concept names defined in `concepts.json`. Writes one structured JSON record to `results/ner/` via `write_ner.py`. Use this skill whenever the user asks to extract entities from a clinical / biomedical text and normalize them against the BSO-AD ontology — phrased as "annotate this abstract", "extract entities from this note", "normalize these mentions", or "run NER on PMID 12345".

2026-07-09
bso-ad
기타 생물 과학자

Extracts named entities from a biomedical / clinical text (PubMed abstract, EHR note, etc.) and normalizes each one to a concept_name from the BSO-AD ontology. Uses MCP tools on the `ner_mcp` server to enumerate the supported entity types, fetch the concept subtree for each type, and map (entity_type, candidate_label) tuples to canonical concept names defined in `concepts.json`. Writes one structured JSON record to `results/ner/` via `write_ner.py`. Use this skill whenever the user asks to extract entities from a clinical / biomedical text and normalize them against the BSO-AD ontology — phrased as "annotate this abstract", "extract entities from this note", "normalize these mentions", or "run NER on PMID 12345".

2026-07-08
이 저장소에서 수집된 skills 32개 중 상위 8개를 표시합니다.
저장소 2개 중 2개 표시
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