medical-data-api
Access FDA drug data and WHO global health statistics for research
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Access FDA drug data and WHO global health statistics for research
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Baseado na classificação ocupacional SOC
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
公司金融实证研究的"漏斗式选题查找器"。互动开场先后询问 (1) 研究方向、(2) 候选标题数量 N, 再扫描全球文献(已出版英文学术期刊 + SSRN working paper + 全球高校 department seminar 1 年内日程),基于 Edmans (2024) "1000 Rejections" 红线生成 N 个候选标题,**通过并行 subagent(Agent 工具)批量生成计划书 + 查新;每个 subagent 必须强制调用 Skill 工具加载 econfin-proposal 与 novelty-check 两个预设 skill 完成各自模块**,**只有当 novelty score >= 9 时(即 JF/JFE/RFS 顶刊层次),subagent 才把 proposal + 查新报告合并的 md 写入 F:\Dropbox\CC\选题大全\<研究方向短名>\(以"简短选题名称-分数"命名,子文件夹名由 Step 0 从用户输入的研究方向派生);< 9 分的选题在 subagent 内部直接丢弃,绝不写盘、绝不输出**。当用户说"找选题"、"帮我找选题"、"想做 X 方向"、 "empirical CF idea search"、"批量生成研究计划书"、"100 ideas"、"econfin-idea-finder" 时触发。
Create and compile beautiful Beamer presentations following the Rhetoric of Decks philosophy. Use when making slides, creating decks, or compiling .tex presentation files.
Scaffold a new research project with standard directory structure, CLAUDE.md template, and documented README. Use this at the start of every new project to ensure consistent organization.
Download, split, and deeply read academic PDFs. Use when asked to read, review, or summarize an academic paper. Splits PDFs into 4-page chunks, reads them in small batches, and produces structured reading notes — avoiding context window crashes and shallow comprehension.
This skill should be used when the user asks to "create a slash command", "add a command", "write a custom command", "define command arguments", "use command frontmatter", "organize commands", "create command with file references", "interactive command", "use AskUserQuestion in command", or needs guidance on slash command structure, YAML frontmatter fields, dynamic arguments, bash execution in commands, user interaction patterns, or command development best practices for Claude Code.
| name | medical-data-api |
| description | Access FDA drug data and WHO global health statistics for research |
| metadata | {"openclaw":{"emoji":"💊","category":"domains","subcategory":"biomedical","keywords":["FDA","drug safety","WHO","global health","adverse events","medical data","pharmacovigilance"],"source":"https://open.fda.gov"}} |
This skill covers two major open medical data APIs for academic research:
openFDA is the U.S. Food and Drug Administration's public API providing access to drug labeling (SPL), adverse event reports (FAERS), recalls, and NDC directory. The FAERS dataset contains over 722,000 reports for common drugs like aspirin, making it a primary pharmacovigilance resource.
WHO Global Health Observatory (GHO) is the WHO's OData v4 API serving over 2,000 health indicators across 194 member states -- life expectancy, mortality, disease burden, health system coverage, risk factors, and SDG targets. Returns structured JSON with numeric values, confidence intervals, and dimensional breakdowns by country, sex, and year.
Both APIs are free, require no authentication, and return JSON.
openFDA: No authentication required. An optional API key (free, via https://open.fda.gov/apis/authentication/) increases rate limits from 240/min to 120,000/day. Register at https://open.fda.gov/apis/ to obtain a key, then append &api_key=YOUR_KEY to requests.
WHO GHO: No authentication required. No API key needed. All endpoints are publicly accessible with no registration.
Search FDA-approved drug labeling data (Structured Product Labeling). Returns boxed warnings, indications, dosage, contraindications, and adverse reactions text.
GET https://api.fda.gov/drug/label.json| Parameter | Type | Required | Description |
|---|---|---|---|
| search | string | No | Search query using openFDA query syntax |
| limit | int | No | Number of results (default 1, max 1000) |
| skip | int | No | Offset for pagination |
| count | string | No | Count unique values of a field |
curl "https://api.fda.gov/drug/label.json?search=aspirin&limit=1"
meta.results.total (26,564 for "aspirin") and results array. Each result contains boxed_warning, indications_and_usage, dosage_and_administration, warnings, adverse_reactions, drug_interactions, and openfda cross-references (brand/generic names, manufacturer, NDC, pharmacologic class).Search the FDA Adverse Event Reporting System. Each record describes a safety report including patient demographics, suspect drugs, reported reactions, and outcomes.
GET https://api.fda.gov/drug/event.json| Parameter | Type | Required | Description |
|---|---|---|---|
| search | string | No | Query (e.g., patient.drug.openfda.brand_name:"aspirin") |
| limit | int | No | Number of results (default 1, max 1000) |
| skip | int | No | Offset for pagination (max skip+limit = 26,000) |
| count | string | No | Count field values (e.g., patient.reaction.reactionmeddrapt.exact) |
curl 'https://api.fda.gov/drug/event.json?search=patient.drug.openfda.brand_name:"aspirin"&limit=1'
meta.results.total (722,607 for aspirin). Each result: safetyreportid, serious (1=yes, 2=no), primarysourcecountry, receivedate, nested patient with patientsex, reaction array (MedDRA terms + reactionoutcome), and drug array with drugcharacterization (1=suspect, 2=concomitant, 3=interacting), medicinalproduct, openfda cross-references.Retrieve data points for a specific health indicator with country, year, and sex dimensions.
GET https://ghoapi.azureedge.net/api/{IndicatorCode}| Parameter | Type | Required | Description |
|---|---|---|---|
| $top | int | No | Limit number of records returned |
| $skip | int | No | Skip records for pagination |
| $filter | string | No | OData filter (e.g., SpatialDim eq 'USA' and TimeDim eq 2020) |
| $select | string | No | Select specific fields |
| $orderby | string | No | Sort results |
# Life expectancy at birth (WHOSIS_000001)
curl "https://ghoapi.azureedge.net/api/WHOSIS_000001?\$top=2"
value array. Each record: SpatialDim (ISO country, e.g., "BTN"), ParentLocation (WHO region), TimeDim (year), Dim1 (sex: "SEX_BTSX"/"SEX_MLE"/"SEX_FMLE"), Value ("67.8 [67.1-68.6]"), NumericValue (67.845665), Low/High confidence bounds.List available health indicators with their codes and names.
GET https://ghoapi.azureedge.net/api/Indicatorcurl "https://ghoapi.azureedge.net/api/Indicator?\$top=5"
IndicatorCode (e.g., "Adult_curr_e-cig"), IndicatorName (e.g., "Prevalence of current e-cigarette use among adults (%)"), Language ("EN"). Over 2,000 indicators spanning mortality, morbidity, health systems, and risk factors.openFDA:
skip + limit cannot exceed 26,000 (use search + sort for deeper access)WHO GHO:
Count reactions by MedDRA term to identify safety signals:
import requests
resp = requests.get("https://api.fda.gov/drug/event.json", params={
"search": 'patient.drug.openfda.brand_name:"aspirin"',
"count": "patient.reaction.reactionmeddrapt.exact",
"limit": 10
})
for r in resp.json()["results"]:
print(f" {r['term']}: {r['count']} reports")
Compare health indicators across countries and time periods:
import requests
resp = requests.get("https://ghoapi.azureedge.net/api/WHOSIS_000001", params={
"$filter": "SpatialDim eq 'JPN' and Dim1 eq 'SEX_BTSX'",
"$orderby": "TimeDim desc",
"$top": 10
})
for row in resp.json()["value"]:
print(f" Japan {row['TimeDim']}: {row['Value']}")
Compare safety language across drug labels for regulatory research:
import requests
for drug in ["ibuprofen", "naproxen", "celecoxib"]:
resp = requests.get("https://api.fda.gov/drug/label.json",
params={"search": f'openfda.generic_name:"{drug}"', "limit": 1})
results = resp.json().get("results", [])
if results:
warning = results[0].get("boxed_warning", ["None"])[0][:200]
print(f"{drug.upper()}: {warning}...\n")
| Code | Indicator |
|---|---|
| WHOSIS_000001 | Life expectancy at birth |
| NCDMORT3070 | NCD mortality (30-70 years) |
| MDG_0000000001 | Under-five mortality rate |
| WHS4_100 | Physicians per 10,000 population |
| NCD_BMI_30A | Prevalence of obesity (BMI >= 30) |
| SA_0000001688 | Alcohol per capita consumption |
| TOBACCO_0000000262 | Tobacco smoking prevalence |