# 🔬 PubMed Research Search — Medical Papers & Data (`nexgendata/pubmed-research-search`) Actor Search PubMed's 35M+ biomedical research papers. Extract abstracts, authors, citations, MeSH terms, and publication data. Ideal for literature reviews, meta-analyses, and medical research. - **URL**: https://apify.com/nexgendata/pubmed-research-search.md - **Developed by:** [Stephan Corbeil](https://apify.com/nexgendata) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN 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 ## PubMed Research Search by nexgendata Search the PubMed database for medical and scientific research papers. This actor queries the National Center for Biotechnology Information (NCBI) E-utilities API to find published research by keyword, author, journal, or MeSH term. Each result includes the paper title, full author list, journal name, publication date, DOI, and PubMed URL. Built for researchers, clinicians, pharmaceutical companies, and anyone who needs programmatic access to the world's largest biomedical literature database. PubMed indexes over 36 million citations from MEDLINE, life science journals, and online books. It covers biomedical and life science research dating back to the 1960s, with the majority of records including abstracts. For systematic reviews, meta-analyses, competitive intelligence in pharma, and evidence-based medicine, PubMed is the primary data source. This actor eliminates the manual process of searching PubMed's web interface and copying results into spreadsheets or databases. ### How It Works Provide a search query using natural language or PubMed's advanced search syntax. The actor searches the PubMed database via NCBI's E-utilities API and retrieves detailed summary data for each matching paper. Results include the PubMed ID (PMID), title, complete author list, journal name, publication date, volume, issue, pages, DOI, publication type, and a direct URL to the paper on PubMed. The data comes back as structured JSON, ready for import into research tools, reference managers, or databases. PubMed search syntax gives you fine-grained control. Use boolean operators (AND, OR, NOT) to combine terms. Add field tags like [Author], [Journal], [MeSH Terms], or [Title/Abstract] to target specific fields. Date ranges work with the format YYYY/MM/DD. For example, searching "CRISPR gene therapy[Title] AND 2024[Date - Publication]" returns only papers with those exact terms published in 2024. The actor passes your query directly to the PubMed search engine, so any syntax that works on pubmed.ncbi.nlm.nih.gov works here. ### Who Uses This Pharmaceutical researchers use PubMed searches to conduct literature reviews for drug development programs, identify prior art, and track competitor publications. Clinical researchers run systematic searches to gather evidence for meta-analyses and clinical practice guidelines. Medical device companies search for clinical evidence supporting their product claims and monitor published studies mentioning their devices or competitor products. Academic researchers need comprehensive literature searches for grant applications, thesis work, and publication preparation. Science journalists track new publications in specific fields. Patent attorneys search biomedical literature for prior art. Health technology assessment agencies review published clinical evidence to inform coverage and reimbursement decisions. Biotech investors monitor publication activity from companies in their portfolio and from competing research groups. Contract research organizations track publications related to their therapeutic areas to stay current on methodology and findings. Medical affairs teams at pharmaceutical companies use PubMed to identify key opinion leaders based on publication volume and citation metrics. ### Pricing This actor runs at $3 per 1,000 results. A literature search returning 100 papers costs $0.30. A comprehensive systematic review pulling 2,000 citations costs $6. Monthly monitoring of 50 search topics with 20 new results each costs $3/month. All data comes from the NCBI public API, which requires no authentication and has no usage fees — you pay only for the Apify compute that runs the actor. # Actor input Schema ## `query` (type: `string`): Medical or scientific search terms. Supports PubMed search syntax including MeSH terms, boolean operators (AND, OR, NOT), and field tags. ## `maxResults` (type: `integer`): Maximum number of papers to return (1-100). ## Actor input object example ```json { "query": "machine learning cancer diagnosis", "maxResults": 20 } ```` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "query": "machine learning cancer diagnosis" }; // Run the Actor and wait for it to finish const run = await client.actor("nexgendata/pubmed-research-search").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "query": "machine learning cancer diagnosis" } # Run the Actor and wait for it to finish run = client.actor("nexgendata/pubmed-research-search").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 '{ "query": "machine learning cancer diagnosis" }' | apify call nexgendata/pubmed-research-search --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nexgendata/pubmed-research-search", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "🔬 PubMed Research Search — Medical Papers & Data", "description": "Search PubMed's 35M+ biomedical research papers. Extract abstracts, authors, citations, MeSH terms, and publication data. Ideal for literature reviews, meta-analyses, and medical research.", "version": "0.0", "x-build-id": "bqSXmU0nx9thILvGf" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nexgendata~pubmed-research-search/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nexgendata-pubmed-research-search", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nexgendata~pubmed-research-search/runs": { "post": { "operationId": "runs-sync-nexgendata-pubmed-research-search", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nexgendata~pubmed-research-search/run-sync": { "post": { "operationId": "run-sync-nexgendata-pubmed-research-search", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "query" ], "properties": { "query": { "title": "Search Query", "type": "string", "description": "Medical or scientific search terms. Supports PubMed search syntax including MeSH terms, boolean operators (AND, OR, NOT), and field tags." }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of papers to return (1-100).", "default": 20 } } }, "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 } } } } } } } } } } ```