# FDA Adverse Events โ€” FAERS & MAUDE Records API (`nexgendata/fda-adverse-events-faers-maude`) Actor Signal-scored adverse-event feed from openFDA FAERS (drugs) + MAUDE (devices). Returns reports with seriousness flags, MedDRA Preferred Terms, and PRR disproportionality signal per drug-event pair. For pharmacovigilance, mass-tort plaintiff intake, device compliance, medical journalism. - **URL**: https://apify.com/nexgendata/fda-adverse-events-faers-maude.md - **Developed by:** [NexGenData](https://apify.com/nexgendata) (community) - **Categories:** Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $100.00 / 1,000 adverse events This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## FDA Adverse Events FDA adverse-event records from FAERS (drugs) and MAUDE (devices) โ€” for pharmacovigilance signal detection. > Public regulatory/health data โ€” not medical advice. ### ๐Ÿ“Š What you get Clean JSON, one record per report (full output has 21 fields): - `source` โ€” Source - `report_id` โ€” Report id - `report_type` โ€” Report type - `received_date` โ€” Received date - `occur_country` โ€” Occur country - `reporter_country` โ€” Reporter country - `serious` โ€” Serious - `seriousness_flags` โ€” Seriousness flags - `patient_age` โ€” Patient age - `patient_age_unit` โ€” Patient age unit **Pricing:** $0.100 per report (Pay-Per-Event) โ€” about **10 reports per $1**. ### ๐Ÿค– Use with AI agents Point Claude, the OpenAI Agents SDK, an n8n flow or any MCP-aware client at it and pull official records on demand. **Agentic payments (x402):** Supports agentic payment via x402 โ€” agents can call this actor with USDC, no API key required. ### ๐Ÿ”— Related regulatory actors **FDA & public-health records family:** [OpenFDA Drug](https://apify.com/nexgendata/openfda-drug-search) ยท [Drug Approvals](https://apify.com/nexgendata/fda-drug-approvals) ยท [Drug Shortages](https://apify.com/nexgendata/fda-drug-shortages-monitor) ยท [Purple Book](https://apify.com/nexgendata/fda-purple-book-biologics-biosimilars) ยท [Warning Letters/483](https://apify.com/nexgendata/fda-warning-letters-483-inspections) ยท [Pharma Pipeline](https://apify.com/nexgendata/fda-pharma-catalyst-pipeline) ยท [Medical Devices](https://apify.com/nexgendata/medical-device-fda-intelligence) **Product-safety recalls:** [NHTSA Recalls](https://apify.com/nexgendata/nhtsa-us-vehicle-recalls) ยท [NHTSA Defects](https://apify.com/nexgendata/nhtsa-vehicle-recall-defect-tracker) ยท [NHTSA Safety](https://apify.com/nexgendata/nhtsa-vehicle-safety-intelligence) ยท [CPSC Recalls](https://apify.com/nexgendata/cpsc-product-recalls) **Agent front door โ€” Healthcare & FDA MCP:** [Healthcare & FDA MCP](https://apify.com/nexgendata/healthcare-fda-intelligence-mcp) --- *Public official records and enforcement data, assembled for research and monitoring.* # Actor input Schema ## `event_type` (type: `string`): Which adverse-event database(s) to query. 'drug' = FAERS (drug events); 'device' = MAUDE (medical device events); 'both' = combined. ## `search_term` (type: `string`): Free-text Lucene query against drug/device names โ€” matches medicinalproduct, brand_name, generic_name (FAERS) and device.brand_name / device.generic_name (MAUDE). Examples: 'ozempic', 'pacemaker', 'metformin'. ## `serious_only` (type: `boolean`): If true, filter to only serious reports โ€” FAERS serious=1 and MAUDE event_type Injury / Death. Recommended for signal detection. ## `days_back` (type: `integer`): Look-back window from today in days. FAERS uses receivedate; MAUDE uses date_received. Default 90 days. ## `max_events` (type: `integer`): Hard cap on total events returned across both sources. When event_type='both', budget splits 50/50. ## Actor input object example ```json { "event_type": "drug", "search_term": "ozempic", "serious_only": true, "days_back": 90, "max_events": 100 } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "event_type": "drug", "search_term": "ozempic", "serious_only": true, "days_back": 90, "max_events": 100 }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/fda-adverse-events-faers-maude").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "event_type": "drug", "search_term": "ozempic", "serious_only": True, "days_back": 90, "max_events": 100, } # Run the Actor and wait for it to finish run = client.actor("nexgendata/fda-adverse-events-faers-maude").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "event_type": "drug", "search_term": "ozempic", "serious_only": true, "days_back": 90, "max_events": 100 }' | apify call nexgendata/fda-adverse-events-faers-maude --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/fda-adverse-events-faers-maude", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "FDA Adverse Events โ€” FAERS & MAUDE Records API", "description": "Signal-scored adverse-event feed from openFDA FAERS (drugs) + MAUDE (devices). Returns reports with seriousness flags, MedDRA Preferred Terms, and PRR disproportionality signal per drug-event pair. For pharmacovigilance, mass-tort plaintiff intake, device compliance, medical journalism.", "version": "0.0", "x-build-id": "BAdAybafBFNjgMnMJ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~fda-adverse-events-faers-maude/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-fda-adverse-events-faers-maude", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~fda-adverse-events-faers-maude/runs": { "post": { "operationId": "runs-sync-nexgendata-fda-adverse-events-faers-maude", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~fda-adverse-events-faers-maude/run-sync": { "post": { "operationId": "run-sync-nexgendata-fda-adverse-events-faers-maude", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "event_type": { "title": "Event Source", "enum": [ "drug", "device", "both" ], "type": "string", "description": "Which adverse-event database(s) to query. 'drug' = FAERS (drug events); 'device' = MAUDE (medical device events); 'both' = combined.", "default": "both" }, "search_term": { "title": "Search Term", "type": "string", "description": "Free-text Lucene query against drug/device names โ€” matches medicinalproduct, brand_name, generic_name (FAERS) and device.brand_name / device.generic_name (MAUDE). Examples: 'ozempic', 'pacemaker', 'metformin'." }, "serious_only": { "title": "Serious Events Only", "type": "boolean", "description": "If true, filter to only serious reports โ€” FAERS serious=1 and MAUDE event_type Injury / Death. Recommended for signal detection.", "default": true }, "days_back": { "title": "Days Back", "minimum": 1, "maximum": 3650, "type": "integer", "description": "Look-back window from today in days. FAERS uses receivedate; MAUDE uses date_received. Default 90 days.", "default": 90 }, "max_events": { "title": "Max Events", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Hard cap on total events returned across both sources. When event_type='both', budget splits 50/50.", "default": 200 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```