[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6EC6DJQZSFGTHTY2XP63M6G":3,"skill-same:01M6EC6DJQZSFGTHTY2XP63M6G":107},{"skillNo":4,"name":5,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":7,"ownerLogin":8,"ownerAvatarUrl":9,"ownerAccountNo":10,"ownerDisplayName":11,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":26,"linkOnly":27,"markdownContent":28,"markdownBody":28,"frontMatter":29,"refName":30,"commitSha":31,"skillDirectory":32,"forksCount":33,"repositoryHtmlUrl":34,"repositoryHomepageUrl":35,"ownerHtmlUrl":36,"ownerBlogUrl":6,"ownerDescription":37,"updatedAt":38,"install":39,"resources":52},"01M6EC6DJQZSFGTHTY2XP63M6G","fda-database",null,"ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",66,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002FK-Dense-AI\u002Fclaude-scientific-skills\u002Ffda-database\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[23,24,25],"database","fda database","fda","2026-11-10 10:12:30",false,"# FDA Database Access\n\n## Overview\n\nAccess comprehensive FDA regulatory data through openFDA, the FDA's initiative to provide open APIs for public datasets. Query information about drugs, medical devices, foods, animal\u002Fveterinary products, and substances using Python with standardized interfaces.\n\n**Key capabilities:**\n- Query adverse events for drugs, devices, foods, and veterinary products\n- Access product labeling, approvals, and regulatory submissions\n- Monitor recalls and enforcement actions\n- Look up National Drug Codes (NDC) and substance identifiers (UNII)\n- Analyze device classifications and clearances (510k, PMA)\n- Track drug shortages and supply issues\n- Research chemical structures and substance relationships\n\n## When to Use This Skill\n\nThis skill should be used when working with:\n- **Drug research**: Safety profiles, adverse events, labeling, approvals, shortages\n- **Medical device surveillance**: Adverse events, recalls, 510(k) clearances, PMA approvals\n- **Food safety**: Recalls, allergen tracking, adverse events, dietary supplements\n- **Veterinary medicine**: Animal drug adverse events by species and breed\n- **Chemical\u002Fsubstance data**: UNII lookup, CAS number mapping, molecular structures\n- **Regulatory analysis**: Approval pathways, enforcement actions, compliance tracking\n- **Pharmacovigilance**: Post-market surveillance, safety signal detection\n- **Scientific research**: Drug interactions, comparative safety, epidemiological studies\n\n## Quick Start\n\n### 1. Basic Setup\n\n```python\nfrom scripts.fda_query import FDAQuery\n\n# Initialize (API key optional but recommended)\nfda = FDAQuery(api_key=\"YOUR_API_KEY\")\n\n# Query drug adverse events\nevents = fda.query_drug_events(\"aspirin\", limit=100)\n\n# Get drug labeling\nlabel = fda.query_drug_label(\"Lipitor\", brand=True)\n\n# Search device recalls\nrecalls = fda.query(\"device\", \"enforcement\",\n                   search=\"classification:Class+I\",\n                   limit=50)\n```\n\n### 2. API Key Setup\n\nWhile the API works without a key, registering provides higher rate limits:\n- **Without key**: 240 requests\u002Fmin, 1,000\u002Fday\n- **With key**: 240 requests\u002Fmin, 120,000\u002Fday\n\nRegister at: https:\u002F\u002Fopen.fda.gov\u002Fapis\u002Fauthentication\u002F\n\nSet as environment variable:\n```bash\nexport FDA_API_KEY=\"your_key_here\"\n```\n\n### 3. Running Examples\n\n```bash\n# Run comprehensive examples\npython scripts\u002Ffda_examples.py\n\n# This demonstrates:\n# - Drug safety profiles\n# - Device surveillance\n# - Food recall monitoring\n# - Substance lookup\n# - Comparative drug analysis\n# - Veterinary drug analysis\n```\n\n## FDA Database Categories\n\n### Drugs\n\nAccess 6 drug-related endpoints covering the full drug lifecycle from approval to post-market surveillance.\n\n**Endpoints:**\n1. **Adverse Events** - Reports of side effects, errors, and therapeutic failures\n2. **Product Labeling** - Prescribing information, warnings, indications\n3. **NDC Directory** - National Drug Code product information\n4. **Enforcement Reports** - Drug recalls and safety actions\n5. **Drugs@FDA** - Historical approval data since 1939\n6. **Drug Shortages** - Current and resolved supply issues\n\n**Common use cases:**\n```python\n# Safety signal detection\nfda.count_by_field(\"drug\", \"event\",\n                  search=\"patient.drug.medicinalproduct:metformin\",\n                  field=\"patient.reaction.reactionmeddrapt\")\n\n# Get prescribing information\nlabel = fda.query_drug_label(\"Keytruda\", brand=True)\n\n# Check for recalls\nrecalls = fda.query_drug_recalls(drug_name=\"metformin\")\n\n# Monitor shortages\nshortages = fda.query(\"drug\", \"drugshortages\",\n                     search=\"status:Currently+in+Shortage\")\n```\n\n**Reference:** See `references\u002Fdrugs.md` for detailed documentation\n\n### Devices\n\nAccess 9 device-related endpoints covering medical device safety, approvals, and registrations.\n\n**Endpoints:**\n1. **Adverse Events** - Device malfunctions, injuries, deaths\n2. **510(k) Clearances** - Premarket notifications\n3. **Classification** - Device categories and risk classes\n4. **Enforcement Reports** - Device recalls\n5. **Recalls** - Detailed recall information\n6. **PMA** - Premarket approval data for Class III devices\n7. **Registrations & Listings** - Manufacturing facility data\n8. **UDI** - Unique Device Identification database\n9. **COVID-19 Serology** - Antibody test performance data\n\n**Common use cases:**\n```python\n# Monitor device safety\nevents = fda.query_device_events(\"pacemaker\", limit=100)\n\n# Look up device classification\nclassification = fda.query_device_classification(\"DQY\")\n\n# Find 510(k) clearances\nclearances = fda.query_device_510k(applicant=\"Medtronic\")\n\n# Search by UDI\ndevice_info = fda.query(\"device\", \"udi\",\n                       search=\"identifiers.id:00884838003019\")\n```\n\n**Reference:** See `references\u002Fdevices.md` for detailed documentation\n\n### Foods\n\nAccess 2 food-related endpoints for safety monitoring and recalls.\n\n**Endpoints:**\n1. **Adverse Events** - Food, dietary supplement, and cosmetic events\n2. **Enforcement Reports** - Food product recalls\n\n**Common use cases:**\n```python\n# Monitor allergen recalls\nrecalls = fda.query_food_recalls(reason=\"undeclared peanut\")\n\n# Track dietary supplement events\nevents = fda.query_food_events(\n    industry=\"Dietary Supplements\")\n\n# Find contamination recalls\nlisteria = fda.query_food_recalls(\n    reason=\"listeria\",\n    classification=\"I\")\n```\n\n**Reference:** See `references\u002Ffoods.md` for detailed documentation\n\n### Animal & Veterinary\n\nAccess veterinary drug adverse event data with species-specific information.\n\n**Endpoint:**\n1. **Adverse Events** - Animal drug side effects by species, breed, and product\n\n**Common use cases:**\n```python\n# Species-specific events\ndog_events = fda.query_animal_events(\n    species=\"Dog\",\n    drug_name=\"flea collar\")\n\n# Breed predisposition analysis\nbreed_query = fda.query(\"animalandveterinary\", \"event\",\n    search=\"reaction.veddra_term_name:*seizure*+AND+\"\n           \"animal.breed.breed_component:*Labrador*\")\n```\n\n**Reference:** See `references\u002Fanimal_veterinary.md` for detailed documentation\n\n### Substances & Other\n\nAccess molecular-level substance data with UNII codes, chemical structures, and relationships.\n\n**Endpoints:**\n1. **Substance Data** - UNII, CAS, chemical structures, relationships\n2. **NSDE** - Historical substance data (legacy)\n\n**Common use cases:**\n```python\n# UNII to CAS mapping\nsubstance = fda.query_substance_by_unii(\"R16CO5Y76E\")\n\n# Search by name\nresults = fda.query_substance_by_name(\"acetaminophen\")\n\n# Get chemical structure\nstructure = fda.query(\"other\", \"substance\",\n    search=\"names.name:ibuprofen+AND+substanceClass:chemical\")\n```\n\n**Reference:** See `references\u002Fother.md` for detailed documentation\n\n## Common Query Patterns\n\n### Pattern 1: Safety Profile Analysis\n\nCreate comprehensive safety profiles combining multiple data sources:\n\n```python\ndef drug_safety_profile(fda, drug_name):\n    \"\"\"Generate complete safety profile.\"\"\"\n\n    # 1. Total adverse events\n    events = fda.query_drug_events(drug_name, limit=1)\n    total = events[\"meta\"][\"results\"][\"total\"]\n\n    # 2. Most common reactions\n    reactions = fda.count_by_field(\n        \"drug\", \"event\",\n        search=f\"patient.drug.medicinalproduct:*{drug_name}*\",\n        field=\"patient.reaction.reactionmeddrapt\",\n        exact=True\n    )\n\n    # 3. Serious events\n    serious = fda.query(\"drug\", \"event\",\n        search=f\"patient.drug.medicinalproduct:*{drug_name}*+AND+serious:1\",\n        limit=1)\n\n    # 4. Recent recalls\n    recalls = fda.query_drug_recalls(drug_name=drug_name)\n\n    return {\n        \"total_events\": total,\n        \"top_reactions\": reactions[\"results\"][:10],\n        \"serious_events\": serious[\"meta\"][\"results\"][\"total\"],\n        \"recalls\": recalls[\"results\"]\n    }\n```\n\n### Pattern 2: Temporal Trend Analysis\n\nAnalyze trends over time using date ranges:\n\n```python\nfrom datetime import datetime, timedelta\n\ndef get_monthly_trends(fda, drug_name, months=12):\n    \"\"\"Get monthly adverse event trends.\"\"\"\n    trends = []\n\n    for i in range(months):\n        end = datetime.now() - timedelta(days=30*i)\n        start = end - timedelta(days=30)\n\n        date_range = f\"[{start.strftime('%Y%m%d')}+TO+{end.strftime('%Y%m%d')}]\"\n        search = f\"patient.drug.medicinalproduct:*{drug_name}*+AND+receivedate:{date_range}\"\n\n        result = fda.query(\"drug\", \"event\", search=search, limit=1)\n        count = result[\"meta\"][\"results\"][\"total\"] if \"meta\" in result else 0\n\n        trends.append({\n            \"month\": start.strftime(\"%Y-%m\"),\n            \"events\": count\n        })\n\n    return trends\n```\n\n### Pattern 3: Comparative Analysis\n\nCompare multiple products side-by-side:\n\n```python\ndef compare_drugs(fda, drug_list):\n    \"\"\"Compare safety profiles of multiple drugs.\"\"\"\n    comparison = {}\n\n    for drug in drug_list:\n        # Total events\n        events = fda.query_drug_events(drug, limit=1)\n        total = events[\"meta\"][\"results\"][\"total\"] if \"meta\" in events else 0\n\n        # Serious events\n        serious = fda.query(\"drug\", \"event\",\n            search=f\"patient.drug.medicinalproduct:*{drug}*+AND+serious:1\",\n            limit=1)\n        serious_count = serious[\"meta\"][\"results\"][\"total\"] if \"meta\" in serious else 0\n\n        comparison[drug] = {\n            \"total_events\": total,\n            \"serious_events\": serious_count,\n            \"serious_rate\": (serious_count\u002Ftotal*100) if total > 0 else 0\n        }\n\n    return comparison\n```\n\n### Pattern 4: Cross-Database Lookup\n\nLink data across multiple endpoints:\n\n```python\ndef comprehensive_device_lookup(fda, device_name):\n    \"\"\"Look up device across all relevant databases.\"\"\"\n\n    return {\n        \"adverse_events\": fda.query_device_events(device_name, limit=10),\n        \"510k_clearances\": fda.query_device_510k(device_name=device_name),\n        \"recalls\": fda.query(\"device\", \"enforcement\",\n                           search=f\"product_description:*{device_name}*\"),\n        \"udi_info\": fda.query(\"device\", \"udi\",\n                            search=f\"brand_name:*{device_name}*\")\n    }\n```\n\n## Working with Results\n\n### Response Structure\n\nAll API responses follow this structure:\n\n```python\n{\n    \"meta\": {\n        \"disclaimer\": \"...\",\n        \"results\": {\n            \"skip\": 0,\n            \"limit\": 100,\n            \"total\": 15234\n        }\n    },\n    \"results\": [\n        # Array of result objects\n    ]\n}\n```\n\n### Error Handling\n\nAlways handle potential errors:\n\n```python\nresult = fda.query_drug_events(\"aspirin\", limit=10)\n\nif \"error\" in result:\n    print(f\"Error: {result['error']}\")\nelif \"results\" not in result or len(result[\"results\"]) == 0:\n    print(\"No results found\")\nelse:\n    # Process results\n    for event in result[\"results\"]:\n        # Handle event data\n        pass\n```\n\n### Pagination\n\nFor large result sets, use pagination:\n\n```python\n# Automatic pagination\nall_results = fda.query_all(\n    \"drug\", \"event\",\n    search=\"patient.drug.medicinalproduct:aspirin\",\n    max_results=5000\n)\n\n# Manual pagination\nfor skip in range(0, 1000, 100):\n    batch = fda.query(\"drug\", \"event\",\n                     search=\"...\",\n                     limit=100,\n                     skip=skip)\n    # Process batch\n```\n\n## Best Practices\n\n### 1. Use Specific Searches\n\n**DO:**\n```python\n# Specific field search\nsearch=\"patient.drug.medicinalproduct:aspirin\"\n```\n\n**DON'T:**\n```python\n# Overly broad wildcard\nsearch=\"*aspirin*\"\n```\n\n### 2. Implement Rate Limiting\n\nThe `FDAQuery` class handles rate limiting automatically, but be aware of limits:\n- 240 requests per minute\n- 120,000 requests per day (with API key)\n\n### 3. Cache Frequently Accessed Data\n\nThe `FDAQuery` class includes built-in caching (enabled by default):\n\n```python\n# Caching is automatic\nfda = FDAQuery(api_key=api_key, use_cache=True, cache_ttl=3600)\n```\n\n### 4. Use Exact Matching for Counting\n\nWhen counting\u002Faggregating, use `.exact` suffix:\n\n```python\n# Count exact phrases\nfda.count_by_field(\"drug\", \"event\",\n                  search=\"...\",\n                  field=\"patient.reaction.reactionmeddrapt\",\n                  exact=True)  # Adds .exact automatically\n```\n\n### 5. Validate Input Data\n\nClean and validate search terms:\n\n```python\ndef clean_drug_name(name):\n    \"\"\"Clean drug name for query.\"\"\"\n    return name.strip().replace('\"', '\\\\\"')\n\ndrug_name = clean_drug_name(user_input)\n```\n\n## API Reference\n\nFor detailed information about:\n- **Authentication and rate limits** → See `references\u002Fapi_basics.md`\n- **Drug databases** → See `references\u002Fdrugs.md`\n- **Device databases** → See `references\u002Fdevices.md`\n- **Food databases** → See `references\u002Ffoods.md`\n- **Animal\u002Fveterinary databases** → See `references\u002Fanimal_veterinary.md`\n- **Substance databases** → See `references\u002Fother.md`\n\n## Scripts\n\n### `scripts\u002Ffda_query.py`\n\nMain query module with `FDAQuery` class providing:\n- Unified interface to all FDA endpoints\n- Automatic rate limiting and caching\n- Error handling and retry logic\n- Common query patterns\n\n### `scripts\u002Ffda_examples.py`\n\nComprehensive examples demonstrating:\n- Drug safety profile analysis\n- Device surveillance monitoring\n- Food recall tracking\n- Substance lookup\n- Comparative drug analysis\n- Veterinary drug analysis\n\nRun examples:\n```bash\npython scripts\u002Ffda_examples.py\n```\n\n## Additional Resources\n\n- **openFDA Homepage**: https:\u002F\u002Fopen.fda.gov\u002F\n- **API Documentation**: https:\u002F\u002Fopen.fda.gov\u002Fapis\u002F\n- **Interactive API Explorer**: https:\u002F\u002Fopen.fda.gov\u002Fapis\u002Ftry-the-api\u002F\n- **GitHub Repository**: https:\u002F\u002Fgithub.com\u002FFDA\u002Fopenfda\n- **Terms of Service**: https:\u002F\u002Fopen.fda.gov\u002Fterms\u002F\n\n## Support and Troubleshooting\n\n### Common Issues\n\n**Issue**: Rate limit exceeded\n- **Solution**: Use API key, implement delays, or reduce request frequency\n\n**Issue**: No results found\n- **Solution**: Try broader search terms, check spelling, use wildcards\n\n**Issue**: Invalid query syntax\n- **Solution**: Review query 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