FDA数据库Python访问指南：药品医疗器械不良事件查询与监管数据分析

fda-database by davila7/claude-code-templates

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安装命令

npx skills add https://github.com/davila7/claude-code-templates --skill fda-database

医疗科技数据处理 API

🇨🇳中文介绍

FDA 数据库访问

概述

通过 openFDA（FDA 为公共数据集提供开放 API 的倡议）访问全面的 FDA 监管数据。使用 Python 和标准化接口查询有关药品、医疗器械、食品、动物/兽药产品以及物质的信息。

核心功能：

查询药品、器械、食品和兽药产品的不良事件
访问产品标签、批准信息和监管提交文件
监控召回和执法行动
查找国家药品代码（NDC）和物质标识符（UNII）
分析器械分类和许可（510k、PMA）
跟踪药品短缺和供应问题
研究化学结构和物质关系

何时使用此技能

此技能适用于处理以下情况：

药物研究：安全性概况、不良事件、标签、批准信息、短缺情况
医疗器械监测：不良事件、召回、510(k) 许可、PMA 批准
食品安全：召回、过敏原追踪、不良事件、膳食补充剂
兽医学：按物种和品种划分的动物药品不良事件
化学/物质数据：UNII 查找、CAS 号映射、分子结构
监管分析：批准途径、执法行动、合规性跟踪
药物警戒：上市后监测、安全信号检测
科学研究：药物相互作用、安全性比较、流行病学研究

快速开始

1. 基本设置

from scripts.fda_query import FDAQuery

# 初始化（API 密钥可选但推荐）
fda = FDAQuery(api_key="YOUR_API_KEY")

# 查询药品不良事件
events = fda.query_drug_events("aspirin", limit=100)

# 获取药品标签
label = fda.query_drug_label("Lipitor", brand=True)

# 搜索器械召回
recalls = fda.query("device", "enforcement",
                   search="classification:Class+I",
                   limit=50)

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模式 1：安全性概况分析

结合多个数据源创建全面的安全性概况：

def drug_safety_profile(fda, drug_name):
    """生成完整的安全性概况。"""

    # 1. 总不良事件数
    events = fda.query_drug_events(drug_name, limit=1)
    total = events["meta"]["results"]["total"]

    # 2. 最常见反应
    reactions = fda.count_by_field(
        "drug", "event",
        search=f"patient.drug.medicinalproduct:*{drug_name}*",
        field="patient.reaction.reactionmeddrapt",
        exact=True
    )

    # 3. 严重事件
    serious = fda.query("drug", "event",
        search=f"patient.drug.medicinalproduct:*{drug_name}*+AND+serious:1",
        limit=1)

    # 4. 近期召回
    recalls = fda.query_drug_recalls(drug_name=drug_name)

    return {
        "total_events": total,
        "top_reactions": reactions["results"][:10],
        "serious_events": serious["meta"]["results"]["total"],
        "recalls": recalls["results"]
    }

模式 2：时间趋势分析

使用日期范围分析时间趋势：

from datetime import datetime, timedelta

def get_monthly_trends(fda, drug_name, months=12):
    """获取月度不良事件趋势。"""
    trends = []

    for i in range(months):
        end = datetime.now() - timedelta(days=30*i)
        start = end - timedelta(days=30)

        date_range = f"[{start.strftime('%Y%m%d')}+TO+{end.strftime('%Y%m%d')}]"
        search = f"patient.drug.medicinalproduct:*{drug_name}*+AND+receivedate:{date_range}"

        result = fda.query("drug", "event", search=search, limit=1)
        count = result["meta"]["results"]["total"] if "meta" in result else 0

        trends.append({
            "month": start.strftime("%Y-%m"),
            "events": count
        })

    return trends

模式 3：比较分析

并排比较多个产品：

def compare_drugs(fda, drug_list):
    """比较多种药物的安全性概况。"""
    comparison = {}

    for drug in drug_list:
        # 总事件数
        events = fda.query_drug_events(drug, limit=1)
        total = events["meta"]["results"]["total"] if "meta" in events else 0

        # 严重事件数
        serious = fda.query("drug", "event",
            search=f"patient.drug.medicinalproduct:*{drug}*+AND+serious:1",
            limit=1)
        serious_count = serious["meta"]["results"]["total"] if "meta" in serious else 0

        comparison[drug] = {
            "total_events": total,
            "serious_events": serious_count,
            "serious_rate": (serious_count/total*100) if total > 0 else 0
        }

    return comparison

模式 4：跨数据库查找

跨多个端点链接数据：

def comprehensive_device_lookup(fda, device_name):
    """在所有相关数据库中查找器械。"""

    return {
        "adverse_events": fda.query_device_events(device_name, limit=10),
        "510k_clearances": fda.query_device_510k(device_name=device_name),
        "recalls": fda.query("device", "enforcement",
                           search=f"product_description:*{device_name}*"),
        "udi_info": fda.query("device", "udi",
                            search=f"brand_name:*{device_name}*")
    }

所有 API 响应都遵循此结构：

{
    "meta": {
        "disclaimer": "...",
        "results": {
            "skip": 0,
            "limit": 100,
            "total": 15234
        }
    },
    "results": [
        # 结果对象数组
    ]
}

始终处理潜在错误：

result = fda.query_drug_events("aspirin", limit=10)

if "error" in result:
    print(f"错误：{result['error']}")
elif "results" not in result or len(result["results"]) == 0:
    print("未找到结果")
else:
    # 处理结果
    for event in result["results"]:
        # 处理事件数据
        pass

对于大型结果集，使用分页：

# 自动分页
all_results = fda.query_all(
    "drug", "event",
    search="patient.drug.medicinalproduct:aspirin",
    max_results=5000
)

# 手动分页
for skip in range(0, 1000, 100):
    batch = fda.query("drug", "event",
                     search="...",
                     limit=100,
                     skip=skip)
    # 处理批次

1. 使用具体搜索

# 具体字段搜索
search="patient.drug.medicinalproduct:aspirin"

# 过于宽泛的通配符
search="*aspirin*"

2. 实施速率限制

FDAQuery 类会自动处理速率限制，但请注意限制：

每分钟 240 次请求
每天 120,000 次请求（使用 API 密钥）

3. 缓存频繁访问的数据

FDAQuery 类包含内置缓存（默认启用）：

# 缓存是自动的
fda = FDAQuery(api_key=api_key, use_cache=True, cache_ttl=3600)

4. 计数时使用精确匹配

计数/聚合时，使用 .exact 后缀：

# 计算精确短语
fda.count_by_field("drug", "event",
                  search="...",
                  field="patient.reaction.reactionmeddrapt",
                  exact=True)  # 自动添加 .exact

5. 验证输入数据

清理和验证搜索词：

def clean_drug_name(name):
    """清理查询的药品名称。"""
    return name.strip().replace('"', '\\"')

drug_name = clean_drug_name(user_input)

有关以下内容的详细信息：

身份验证和速率限制 → 参见 references/api_basics.md
药品数据库 → 参见 references/drugs.md
器械数据库 → 参见 references/devices.md
食品数据库 → 参见 references/foods.md
动物/兽药数据库 → 参见 references/animal_veterinary.md
物质数据库 → 参见 references/other.md

`scripts/fda_query.py`

主要查询模块，提供 FDAQuery 类：

所有 FDA 端点的统一接口
自动速率限制和缓存
错误处理和重试逻辑
常见查询模式

`scripts/fda_examples.py`

综合示例，演示：

药品安全性概况分析
器械监测监控
食品召回跟踪
物质查找
药品比较分析
兽药分析

python scripts/fda_examples.py

openFDA 主页：https://open.fda.gov/
API 文档：https://open.fda.gov/apis/
交互式 API 浏览器：https://open.fda.gov/apis/try-the-api/
GitHub 仓库：https://github.com/FDA/openfda
服务条款：https://open.fda.gov/terms/

支持与故障排除

问题：超出速率限制

解决方案：使用 API 密钥、实施延迟或降低请求频率

问题：未找到结果

解决方案：尝试更广泛的搜索词、检查拼写、使用通配符

问题：查询语法无效

解决方案：查看 references/api_basics.md 中的查询语法

问题：结果中缺少字段

解决方案：并非所有记录都包含所有字段；始终检查字段是否存在

GitHub Issues：https://github.com/FDA/openfda/issues
电子邮件：open-fda@fda.hhs.gov

🇺🇸English

FDA Database Access

Overview

Access 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/veterinary products, and substances using Python with standardized interfaces.

Key capabilities:

Query adverse events for drugs, devices, foods, and veterinary products
Access product labeling, approvals, and regulatory submissions
Monitor recalls and enforcement actions
Look up National Drug Codes (NDC) and substance identifiers (UNII)
Analyze device classifications and clearances (510k, PMA)
Track drug shortages and supply issues
Research chemical structures and substance relationships

When to Use This Skill

This skill should be used when working with:

Drug research : Safety profiles, adverse events, labeling, approvals, shortages
Medical device surveillance : Adverse events, recalls, 510(k) clearances, PMA approvals
Food safety : Recalls, allergen tracking, adverse events, dietary supplements
Veterinary medicine : Animal drug adverse events by species and breed
Chemical/substance data : UNII lookup, CAS number mapping, molecular structures
Regulatory analysis : Approval pathways, enforcement actions, compliance tracking
Pharmacovigilance : Post-market surveillance, safety signal detection
Scientific research : Drug interactions, comparative safety, epidemiological studies

Quick Start

1. Basic Setup

from scripts.fda_query import FDAQuery

# Initialize (API key optional but recommended)
fda = FDAQuery(api_key="YOUR_API_KEY")

# Query drug adverse events
events = fda.query_drug_events("aspirin", limit=100)

# Get drug labeling
label = fda.query_drug_label("Lipitor", brand=True)

# Search device recalls
recalls = fda.query("device", "enforcement",
                   search="classification:Class+I",
                   limit=50)

2. API Key Setup

While the API works without a key, registering provides higher rate limits:

Without key : 240 requests/min, 1,000/day
With key : 240 requests/min, 120,000/day

Set as environment variable:

export FDA_API_KEY="your_key_here"

3. Running Examples

# Run comprehensive examples
python scripts/fda_examples.py

# This demonstrates:
# - Drug safety profiles
# - Device surveillance
# - Food recall monitoring
# - Substance lookup
# - Comparative drug analysis
# - Veterinary drug analysis

FDA Database Categories

Drugs

Access 6 drug-related endpoints covering the full drug lifecycle from approval to post-market surveillance.

Endpoints:

Adverse Events - Reports of side effects, errors, and therapeutic failures
Product Labeling - Prescribing information, warnings, indications
NDC Directory - National Drug Code product information
Enforcement Reports - Drug recalls and safety actions
Drugs@FDA - Historical approval data since 1939
Drug Shortages - Current and resolved supply issues

Common use cases:

# Safety signal detection
fda.count_by_field("drug", "event",
                  search="patient.drug.medicinalproduct:metformin",
                  field="patient.reaction.reactionmeddrapt")

# Get prescribing information
label = fda.query_drug_label("Keytruda", brand=True)

# Check for recalls
recalls = fda.query_drug_recalls(drug_name="metformin")

# Monitor shortages
shortages = fda.query("drug", "drugshortages",
                     search="status:Currently+in+Shortage")

Reference: See references/drugs.md for detailed documentation

Devices

Access 9 device-related endpoints covering medical device safety, approvals, and registrations.

Endpoints:

Adverse Events - Device malfunctions, injuries, deaths
510(k) Clearances - Premarket notifications
Classification - Device categories and risk classes
Enforcement Reports - Device recalls
Recalls - Detailed recall information
PMA - Premarket approval data for Class III devices
Registrations & Listings - Manufacturing facility data
UDI - Unique Device Identification database
COVID-19 Serology - Antibody test performance data

Common use cases:

# Monitor device safety
events = fda.query_device_events("pacemaker", limit=100)

# Look up device classification
classification = fda.query_device_classification("DQY")

# Find 510(k) clearances
clearances = fda.query_device_510k(applicant="Medtronic")

# Search by UDI
device_info = fda.query("device", "udi",
                       search="identifiers.id:00884838003019")

Reference: See references/devices.md for detailed documentation

Foods

Access 2 food-related endpoints for safety monitoring and recalls.

Endpoints:

Adverse Events - Food, dietary supplement, and cosmetic events
Enforcement Reports - Food product recalls

Common use cases:

# Monitor allergen recalls
recalls = fda.query_food_recalls(reason="undeclared peanut")

# Track dietary supplement events
events = fda.query_food_events(
    industry="Dietary Supplements")

# Find contamination recalls
listeria = fda.query_food_recalls(
    reason="listeria",
    classification="I")

Reference: See references/foods.md for detailed documentation

Animal & Veterinary

Access veterinary drug adverse event data with species-specific information.

Endpoint:

Adverse Events - Animal drug side effects by species, breed, and product

Common use cases:

# Species-specific events
dog_events = fda.query_animal_events(
    species="Dog",
    drug_name="flea collar")

# Breed predisposition analysis
breed_query = fda.query("animalandveterinary", "event",
    search="reaction.veddra_term_name:*seizure*+AND+"
           "animal.breed.breed_component:*Labrador*")

Reference: See references/animal_veterinary.md for detailed documentation

Substances & Other

Access molecular-level substance data with UNII codes, chemical structures, and relationships.

Endpoints:

Substance Data - UNII, CAS, chemical structures, relationships
NSDE - Historical substance data (legacy)

Common use cases:

# UNII to CAS mapping
substance = fda.query_substance_by_unii("R16CO5Y76E")

# Search by name
results = fda.query_substance_by_name("acetaminophen")

# Get chemical structure
structure = fda.query("other", "substance",
    search="names.name:ibuprofen+AND+substanceClass:chemical")

Reference: See references/other.md for detailed documentation

Common Query Patterns

Pattern 1: Safety Profile Analysis

Create comprehensive safety profiles combining multiple data sources:

def drug_safety_profile(fda, drug_name):
    """Generate complete safety profile."""

    # 1. Total adverse events
    events = fda.query_drug_events(drug_name, limit=1)
    total = events["meta"]["results"]["total"]

    # 2. Most common reactions
    reactions = fda.count_by_field(
        "drug", "event",
        search=f"patient.drug.medicinalproduct:*{drug_name}*",
        field="patient.reaction.reactionmeddrapt",
        exact=True
    )

    # 3. Serious events
    serious = fda.query("drug", "event",
        search=f"patient.drug.medicinalproduct:*{drug_name}*+AND+serious:1",
        limit=1)

    # 4. Recent recalls
    recalls = fda.query_drug_recalls(drug_name=drug_name)

    return {
        "total_events": total,
        "top_reactions": reactions["results"][:10],
        "serious_events": serious["meta"]["results"]["total"],
        "recalls": recalls["results"]
    }

Pattern 2: Temporal Trend Analysis

Analyze trends over time using date ranges:

from datetime import datetime, timedelta

def get_monthly_trends(fda, drug_name, months=12):
    """Get monthly adverse event trends."""
    trends = []

    for i in range(months):
        end = datetime.now() - timedelta(days=30*i)
        start = end - timedelta(days=30)

        date_range = f"[{start.strftime('%Y%m%d')}+TO+{end.strftime('%Y%m%d')}]"
        search = f"patient.drug.medicinalproduct:*{drug_name}*+AND+receivedate:{date_range}"

        result = fda.query("drug", "event", search=search, limit=1)
        count = result["meta"]["results"]["total"] if "meta" in result else 0

        trends.append({
            "month": start.strftime("%Y-%m"),
            "events": count
        })

    return trends

Pattern 3: Comparative Analysis

Compare multiple products side-by-side:

def compare_drugs(fda, drug_list):
    """Compare safety profiles of multiple drugs."""
    comparison = {}

    for drug in drug_list:
        # Total events
        events = fda.query_drug_events(drug, limit=1)
        total = events["meta"]["results"]["total"] if "meta" in events else 0

        # Serious events
        serious = fda.query("drug", "event",
            search=f"patient.drug.medicinalproduct:*{drug}*+AND+serious:1",
            limit=1)
        serious_count = serious["meta"]["results"]["total"] if "meta" in serious else 0

        comparison[drug] = {
            "total_events": total,
            "serious_events": serious_count,
            "serious_rate": (serious_count/total*100) if total > 0 else 0
        }

    return comparison

Pattern 4: Cross-Database Lookup

Link data across multiple endpoints:

def comprehensive_device_lookup(fda, device_name):
    """Look up device across all relevant databases."""

    return {
        "adverse_events": fda.query_device_events(device_name, limit=10),
        "510k_clearances": fda.query_device_510k(device_name=device_name),
        "recalls": fda.query("device", "enforcement",
                           search=f"product_description:*{device_name}*"),
        "udi_info": fda.query("device", "udi",
                            search=f"brand_name:*{device_name}*")
    }

Working with Results

Response Structure

All API responses follow this structure:

{
    "meta": {
        "disclaimer": "...",
        "results": {
            "skip": 0,
            "limit": 100,
            "total": 15234
        }
    },
    "results": [
        # Array of result objects
    ]
}

Error Handling

Always handle potential errors:

result = fda.query_drug_events("aspirin", limit=10)

if "error" in result:
    print(f"Error: {result['error']}")
elif "results" not in result or len(result["results"]) == 0:
    print("No results found")
else:
    # Process results
    for event in result["results"]:
        # Handle event data
        pass

Pagination

For large result sets, use pagination:

# Automatic pagination
all_results = fda.query_all(
    "drug", "event",
    search="patient.drug.medicinalproduct:aspirin",
    max_results=5000
)

# Manual pagination
for skip in range(0, 1000, 100):
    batch = fda.query("drug", "event",
                     search="...",
                     limit=100,
                     skip=skip)
    # Process batch

Best Practices

1. Use Specific Searches

DO:

# Specific field search
search="patient.drug.medicinalproduct:aspirin"

DON'T:

# Overly broad wildcard
search="*aspirin*"

2. Implement Rate Limiting

The FDAQuery class handles rate limiting automatically, but be aware of limits:

240 requests per minute
120,000 requests per day (with API key)

3. Cache Frequently Accessed Data

The FDAQuery class includes built-in caching (enabled by default):

# Caching is automatic
fda = FDAQuery(api_key=api_key, use_cache=True, cache_ttl=3600)

4. Use Exact Matching for Counting

When counting/aggregating, use .exact suffix:

# Count exact phrases
fda.count_by_field("drug", "event",
                  search="...",
                  field="patient.reaction.reactionmeddrapt",
                  exact=True)  # Adds .exact automatically

5. Validate Input Data

Clean and validate search terms:

def clean_drug_name(name):
    """Clean drug name for query."""
    return name.strip().replace('"', '\\"')

drug_name = clean_drug_name(user_input)

API Reference

For detailed information about:

Authentication and rate limits → See references/api_basics.md
Drug databases → See references/drugs.md
Device databases → See references/devices.md
Food databases → See references/foods.md
Animal/veterinary databases → See references/animal_veterinary.md
Substance databases → See references/other.md

Scripts

`scripts/fda_query.py`

Main query module with FDAQuery class providing:

Unified interface to all FDA endpoints
Automatic rate limiting and caching
Error handling and retry logic
Common query patterns

`scripts/fda_examples.py`

Comprehensive examples demonstrating:

Drug safety profile analysis
Device surveillance monitoring
Food recall tracking
Substance lookup
Comparative drug analysis
Veterinary drug analysis

Run examples:

python scripts/fda_examples.py

Additional Resources

openFDA Homepage : https://open.fda.gov/
API Documentation : https://open.fda.gov/apis/
Interactive API Explorer : https://open.fda.gov/apis/try-the-api/
GitHub Repository : https://github.com/FDA/openfda
Terms of Service : https://open.fda.gov/terms/

Support and Troubleshooting

Common Issues

Issue : Rate limit exceeded

Solution : Use API key, implement delays, or reduce request frequency

Issue : No results found

Solution : Try broader search terms, check spelling, use wildcards

Issue : Invalid query syntax

Solution : Review query syntax in references/api_basics.md

Issue : Missing fields in results

Solution : Not all records contain all fields; always check field existence

Getting Help

GitHub Issues : https://github.com/FDA/openfda/issues
Email : open-fda@fda.hhs.gov

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FDA数据库Python访问指南：药品医疗器械不良事件查询与监管数据分析

🇨🇳中文介绍

FDA 数据库访问

概述

何时使用此技能

快速开始

1. 基本设置

相关 Skills

2. API 密钥设置

3. 运行示例

FDA 数据库类别

药品

医疗器械

食品

动物与兽药

物质与其他

常见查询模式

模式 1：安全性概况分析

模式 2：时间趋势分析

模式 3：比较分析

模式 4：跨数据库查找

处理结果

响应结构

错误处理

分页

最佳实践

1. 使用具体搜索

2. 实施速率限制

3. 缓存频繁访问的数据

4. 计数时使用精确匹配

5. 验证输入数据

API 参考

脚本

scripts/fda_query.py

scripts/fda_examples.py

其他资源

支持与故障排除

常见问题

获取帮助

🇺🇸English

FDA Database Access

Overview

When to Use This Skill

Quick Start

1. Basic Setup

2. API Key Setup

3. Running Examples

FDA Database Categories

Drugs

Devices

Foods

Animal & Veterinary

Substances & Other

Common Query Patterns

Pattern 1: Safety Profile Analysis

Pattern 2: Temporal Trend Analysis

Pattern 3: Comparative Analysis

Pattern 4: Cross-Database Lookup

Working with Results

Response Structure

Error Handling

Pagination

Best Practices

1. Use Specific Searches

2. Implement Rate Limiting

3. Cache Frequently Accessed Data

4. Use Exact Matching for Counting

5. Validate Input Data

API Reference

Scripts

scripts/fda_query.py

scripts/fda_examples.py

Additional Resources

Support and Troubleshooting

Common Issues

Getting Help

最新 Skills

`scripts/fda_query.py`

`scripts/fda_examples.py`

`scripts/fda_query.py`

`scripts/fda_examples.py`