# FDA Adverse Events Scraper - FAERS + MAUDE Export (`maydit/us-fda-adverse-events-scraper`) Actor Scrape FDA adverse events from FAERS (drugs) and MAUDE (devices) via openFDA. Deeply nested reports flattened to one clean row each. Export to CSV, JSON, or Excel. - **URL**: https://apify.com/maydit/us-fda-adverse-events-scraper.md - **Developed by:** [Brandt May](https://apify.com/maydit) (community) - **Categories:** Developer tools, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 results 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 Scraper - FAERS + MAUDE Data Export > Pull FDA drug and medical-device adverse event reports from openFDA, flattened to one clean row per report, and export to CSV, JSON, or Excel. ### What it does This **FDA adverse events scraper** queries the official **openFDA API** and returns two kinds of safety reports in one tool: **FAERS** adverse drug reaction reports (20M+) and **MAUDE** medical device event reports (25M+). Unlike drug-only scrapers and bloated "everything openFDA" generalists that dump raw nested JSON, it flattens each deeply nested openFDA report into a single, analysis-ready row - so **FAERS data**, **drug adverse event exports**, and **MAUDE database exports** land in your dataset already cleaned. Filter by product, reaction or device problem, seriousness, country, and received-date window, then export to CSV or Excel for pharmacovigilance, post-market surveillance, and drug safety signal detection. ### Who it's for - **Pharmacovigilance and drug-safety teams** monitoring products for new or serious adverse events - **Medical-device safety, regulatory, and QA teams** tracking device malfunctions and injuries - **Pharma and medtech researchers** running comparative or epidemiologic analyses - **Product-liability and litigation professionals** pulling report narratives and metadata for cases - **Academics and data scientists** who need standardized, flattened adverse-event datasets ### What you get / Output Each report is flattened to one row with these fields: | Field | Description | |---|---| | `reportId` | Unique FDA report identifier | | `receivedDate` | Date the FDA received the report | | `serious` | Seriousness flag (death, hospitalization, life-threatening, disability) | | `products` | Reported product / brand name(s) | | `genericNames` | FDA-enriched generic name(s) | | `manufacturer` | Manufacturer / applicant information | | `indications` | Reported reason the product was used | | `reactions` | MedDRA-coded reactions, with outcome codes | | `productProblems` | Device problem descriptors (device mode) | | `eventType` | Event classification (death / injury / malfunction) | | `deviceClass` | FDA device class / model (device mode) | | `narrative` | Truncated event narrative text | | `patientAge` | Patient age | | `patientSex` | Patient sex | | `occurCountry` | Country where the event occurred | Fields populate according to mode: drug (FAERS) reports emphasize reactions, indications, and demographics; device (MAUDE) reports emphasize product problems, event type, and device class. ### Input / How to query | Input | What it does | |---|---| | `reportType` | `drug` = FAERS drug reports; `device` = MAUDE device reports | | `productName` | Brand or generic drug name (e.g. `OZEMPIC`, `metformin`) or device name (e.g. `insulin pump`, `IMPELLA`); matched against reported and FDA-enriched names. Leave empty for all | | `reactionOrProblem` | Drug mode: MedDRA reaction term (e.g. `Pancreatitis`). Device mode: product problem (e.g. `Battery Problem`). Leave empty for all | | `seriousOnly` | Drug mode: only serious reports (death, hospitalization, life-threatening, disability). Device mode: only Death or Injury events | | `country` | Two-letter country where the event occurred (drug mode only; ignored in device mode) | | `receivedSince` | Only reports received on or after this date (`YYYY-MM-DD`) | | `receivedUntil` | Only reports received on or before this date (`YYYY-MM-DD`) | | `maxResults` | Upper bound on reports returned per run | ### Example use cases - **Drug safety signal detection:** Pull every serious FAERS report for a single drug over the last quarter and scan reaction terms for emerging patterns. - **Device post-market surveillance:** Export MAUDE malfunction and injury events for an insulin pump line to feed a QA / complaint-handling review. - **Product-liability research:** Collect adverse-event narratives, manufacturer, and outcome data tied to a specific product for a lawsuit. - **Competitive intelligence:** Track a competitor's drug or device adverse events and manufacturer trends over a date range. - **Epidemiologic analysis:** Build a standardized, flattened adverse drug reaction dataset filtered by reaction, country, and demographics for a research study. ### Recurring use / scheduling Run it on a **schedule** (daily, weekly, or monthly) to monitor a product for new or serious adverse events - true post-market surveillance without manual polling. Set `receivedSince` to your last run date so each run only pulls newly received reports, then dedupe on `reportId` when appending to your master dataset to keep the history clean. Point the run's dataset at a webhook or your BI tool to get alerted when new serious reports appear. ### How do I download FAERS data without hitting the openFDA 25,000 record API limit? Set your filters (product, reaction, date window, `seriousOnly`) to scope the query, and use `maxResults` plus incremental `receivedSince` / `receivedUntil` windows to page through data across scheduled runs instead of pulling everything at once. Each run writes a clean dataset you can append and dedupe on `reportId`. ### How can I export FDA adverse event reports to CSV or Excel? Every run stores results in an Apify dataset that you export in one click to **CSV, Excel, JSON, or XML**, or pull via the API. Because reports are already flattened to one row each, the CSV opens cleanly in Excel with no nested-JSON wrangling. ### How do I search the MAUDE database for medical device adverse events in bulk? Set `reportType` to `device`, optionally add a `productName` (e.g. `IMPELLA`) and a `reactionOrProblem` device problem (e.g. `Material Rupture`), then raise `maxResults` and use a `receivedSince` / `receivedUntil` window to collect device events in bulk. ### How do I get both FAERS drug and MAUDE device adverse events in one dataset? This is the tool's core differentiator. Run it once with `reportType` = `drug` and once with `reportType` = `device` (or on two schedules) - both write the same flattened schema, so you can merge them into a single combined adverse-events dataset. ### How do I monitor a drug for new adverse event reports (signal detection)? Schedule the Actor with a fixed `productName` and roll `receivedSince` forward each run so it only returns newly received reports. Dedupe on `reportId` and watch the `serious`, `reactions`, and `eventType` fields for emerging safety signals. ### How do I pull only serious adverse events (death, hospitalization, disability) from FAERS? Set `seriousOnly` to `true` in drug mode to return only reports flagged serious - death, hospitalization, life-threatening, or disability. In device mode the same flag returns only Death or Injury events. ### Do I need an API key to access the openFDA adverse events API? No. openFDA adverse-event endpoints are public and this Actor handles the querying for you - you only provide the search filters. You just need an Apify account to run the Actor. ### How do I flatten nested openFDA adverse event JSON into one row per report? That is exactly what this scraper does automatically. openFDA returns deeply nested report objects; the Actor converts each one into a single flat row with the fields listed in the Output table, so you never have to write JSON-flattening code. ### How do I get adverse event data for a product liability lawsuit? Filter by the product (and optionally reaction / problem, seriousness, and date range), then export the dataset. The `narrative`, `manufacturer`, `reactions`, `productProblems`, and `receivedDate` fields give you the report metadata and event descriptions commonly used in forensic and litigation research. ### What is the difference between FAERS and MAUDE data? **FAERS** is the FDA Adverse Event Reporting System for **drugs** - it captures adverse drug reactions, MedDRA-coded reactions, indications, and patient demographics. **MAUDE** is the FDA's **medical device** event database - it captures device malfunctions, injuries, and deaths with product problems and device class. This Actor covers both via the `reportType` input. ### Data source & notes - **Source:** the official, public [openFDA](https://open.fda.gov/) adverse-event endpoints - `drug/event` (FAERS, 20M+ reports) and `device/event` (MAUDE, 25M+ reports). This is public U.S. government data, free to use. - **Cleaning:** deeply nested openFDA report JSON is flattened to one row per report; the `narrative` field is truncated. - **Coverage limits:** the `country` filter applies to drug mode only. Field population depends on what the reporter submitted - adverse-event reports are voluntary and can be incomplete, and a report does not establish that a product caused the event. Data reflects openFDA's own refresh cadence. # Actor input Schema ## `reportType` (type: `string`): Drug = FAERS adverse drug reaction reports (20M+). Device = MAUDE medical-device event reports (25M+). ## `productName` (type: `string`): Drug name (brand or generic, e.g. 'OZEMPIC', 'metformin') or device name (e.g. 'insulin pump', 'IMPELLA'). Matched against reported names and FDA-enriched brand/generic names. Leave empty for all products. ## `reactionOrProblem` (type: `string`): Drug mode: reaction term (MedDRA), e.g. 'Nausea', 'Pancreatitis'. Device mode: product problem, e.g. 'Material Rupture', 'Battery Problem'. Leave empty for all. ## `seriousOnly` (type: `boolean`): Drug mode: only reports marked serious (death, hospitalization, life-threatening, disability). Device mode: only Death or Injury events (excludes malfunctions). ## `country` (type: `string`): Two-letter country where the event occurred, e.g. 'US'. Ignored in device mode. ## `receivedSince` (type: `string`): Only reports the FDA received on or after this date (YYYY-MM-DD). ## `receivedUntil` (type: `string`): Only reports the FDA received on or before this date (YYYY-MM-DD). ## `maxResults` (type: `integer`): Upper bound on reports returned per run. ## Actor input object example ```json { "reportType": "drug", "productName": "OZEMPIC", "seriousOnly": false, "receivedSince": "2026-01-01", "maxResults": 500 } ```` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("maydit/us-fda-adverse-events-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 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); }); // 📚 Want to learn more 📖? Go to → 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 = {} # Run the Actor and wait for it to finish run = client.actor("maydit/us-fda-adverse-events-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call maydit/us-fda-adverse-events-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=maydit/us-fda-adverse-events-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "FDA Adverse Events Scraper - FAERS + MAUDE Export", "description": "Scrape FDA adverse events from FAERS (drugs) and MAUDE (devices) via openFDA. Deeply nested reports flattened to one clean row each. Export to CSV, JSON, or Excel.", "version": "1.0", "x-build-id": "paCgXeCvXn1YPcaq5" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/maydit~us-fda-adverse-events-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-maydit-us-fda-adverse-events-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/maydit~us-fda-adverse-events-scraper/runs": { "post": { "operationId": "runs-sync-maydit-us-fda-adverse-events-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/maydit~us-fda-adverse-events-scraper/run-sync": { "post": { "operationId": "run-sync-maydit-us-fda-adverse-events-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "reportType": { "title": "Report type", "enum": [ "drug", "device" ], "type": "string", "description": "Drug = FAERS adverse drug reaction reports (20M+). Device = MAUDE medical-device event reports (25M+).", "default": "drug" }, "productName": { "title": "Product name", "type": "string", "description": "Drug name (brand or generic, e.g. 'OZEMPIC', 'metformin') or device name (e.g. 'insulin pump', 'IMPELLA'). Matched against reported names and FDA-enriched brand/generic names. Leave empty for all products." }, "reactionOrProblem": { "title": "Reaction / product problem", "type": "string", "description": "Drug mode: reaction term (MedDRA), e.g. 'Nausea', 'Pancreatitis'. Device mode: product problem, e.g. 'Material Rupture', 'Battery Problem'. Leave empty for all." }, "seriousOnly": { "title": "Serious events only", "type": "boolean", "description": "Drug mode: only reports marked serious (death, hospitalization, life-threatening, disability). Device mode: only Death or Injury events (excludes malfunctions).", "default": false }, "country": { "title": "Country (drug mode only)", "type": "string", "description": "Two-letter country where the event occurred, e.g. 'US'. Ignored in device mode." }, "receivedSince": { "title": "Received since", "type": "string", "description": "Only reports the FDA received on or after this date (YYYY-MM-DD)." }, "receivedUntil": { "title": "Received until", "type": "string", "description": "Only reports the FDA received on or before this date (YYYY-MM-DD)." }, "maxResults": { "title": "Max results", "minimum": 1, "type": "integer", "description": "Upper bound on reports returned per run.", "default": 500 } } }, "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 } } } } } } } } } } ```