# PubMed Research Intelligence (`taroyamada/pubmed-research-intelligence`) Actor Search PubMed with official NCBI E-utilities and export article, author, journal, PMID, DOI, publication type, and biomedical topic rows. - **URL**: https://apify.com/taroyamada/pubmed-research-intelligence.md - **Developed by:** [åĪŠéƒŽ åąąį”°](https://apify.com/taroyamada) (community) - **Categories:** Developer tools, Business, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 Intelligence Search PubMed through official NCBI E-utilities and export flattened rows for biomedical articles, authors, and journals. V1 uses JSON ESearch and ESummary endpoints only, so no browser scraping or login is required. ### Inputs | Field | Default | Notes | |---|---:|---| | `searchTerms` | `[]` | PubMed queries such as `cancer immunotherapy`. | | `pmids` | `[]` | Direct PubMed IDs to summarize. | | `fromDate` / `toDate` | empty | Publication date filter, `YYYY`, `YYYY/MM`, or `YYYY/MM/DD`. | | `sort` | `relevance` | `relevance`, `pub_date`, or `most_recent`. | | `maxResultsPerQuery` | `25` | PMIDs retrieved for each query. | | `maxArticles` | `100` | Global unique article cap. | | `email` | empty | Optional NCBI contact email. | | `apiKey` | empty | Optional NCBI API key for higher rate limits. | | `delivery` | `dataset` | `dataset` or `webhook`. | | `dryRun` | `false` | Skip dataset/webhook delivery. | At least one `searchTerms` or `pmids` value is required. ### Dataset Rows `article_summary` - PMID, title, journal, publication date - DOI, PMC, publication types, language - first/last author, author count - PubMed URL and NLM identifiers `author_signal` - PMID, article title, author name - author order, first/last flags, author type `journal_summary` - PMID, article title, journal name/abbreviation - NLM unique ID, ISSN/eISSN, publication date ### Example Input ```json { "searchTerms": ["cancer immunotherapy", "GLP-1 obesity"], "fromDate": "2024", "sort": "relevance", "maxResultsPerQuery": 10, "maxArticles": 20, "delivery": "dataset", "dryRun": false } ```` ### Local Development ```bash npm install npm test node src/index.js ``` ### Limitations - V1 does not parse EFetch XML abstracts. It intentionally uses ESearch/ESummary JSON for reliable deployment without XML dependencies. - NCBI rate limits are stricter without an API key. Use `email` and `apiKey` for larger scheduled jobs. - PubMed metadata can be incomplete for very new records. # Actor input Schema ## `searchTerms` (type: `array`): PubMed search queries such as cancer immunotherapy, GLP-1 obesity, or CRISPR clinical trial. ## `pmids` (type: `array`): Direct PubMed IDs to summarize. ## `fromDate` (type: `string`): Optional publication date floor in YYYY/MM/DD, YYYY/MM, or YYYY. ## `toDate` (type: `string`): Optional publication date ceiling in YYYY/MM/DD, YYYY/MM, or YYYY. ## `sort` (type: `string`): PubMed ESearch sort order. ## `maxResultsPerQuery` (type: `integer`): Maximum PMIDs to retrieve for each search term. ## `maxArticles` (type: `integer`): Global unique article cap. ## `email` (type: `string`): Optional contact email included with E-utilities requests. ## `apiKey` (type: `string`): Optional NCBI API key for higher rate limits. Leave empty for normal runs. ## `tool` (type: `string`): Tool name sent to NCBI E-utilities. ## `timeoutMs` (type: `integer`): Per-request timeout in milliseconds. ## `delivery` (type: `string`): Send flattened rows to the dataset or POST the full payload to a webhook. ## `webhookUrl` (type: `string`): Required when delivery is webhook. ## `dryRun` (type: `boolean`): Run without saving dataset rows or sending a webhook. ## Actor input object example ```json { "searchTerms": [ "cancer immunotherapy", "GLP-1 obesity" ], "sort": "relevance", "maxResultsPerQuery": 25, "maxArticles": 100, "tool": "pubmed-research-intelligence", "timeoutMs": 20000, "delivery": "dataset", "dryRun": false } ``` # 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 = { "searchTerms": [ "cancer immunotherapy", "GLP-1 obesity" ] }; // Run the Actor and wait for it to finish const run = await client.actor("taroyamada/pubmed-research-intelligence").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`ðŸ’ū 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 = { "searchTerms": [ "cancer immunotherapy", "GLP-1 obesity", ] } # Run the Actor and wait for it to finish run = client.actor("taroyamada/pubmed-research-intelligence").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("ðŸ’ū 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 '{ "searchTerms": [ "cancer immunotherapy", "GLP-1 obesity" ] }' | apify call taroyamada/pubmed-research-intelligence --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=taroyamada/pubmed-research-intelligence", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "PubMed Research Intelligence", "description": "Search PubMed with official NCBI E-utilities and export article, author, journal, PMID, DOI, publication type, and biomedical topic rows.", "version": "0.1", "x-build-id": "hrF80u3p9XUS8rm9a" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/taroyamada~pubmed-research-intelligence/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-taroyamada-pubmed-research-intelligence", "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/taroyamada~pubmed-research-intelligence/runs": { "post": { "operationId": "runs-sync-taroyamada-pubmed-research-intelligence", "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/taroyamada~pubmed-research-intelligence/run-sync": { "post": { "operationId": "run-sync-taroyamada-pubmed-research-intelligence", "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": { "searchTerms": { "title": "Search Terms", "type": "array", "description": "PubMed search queries such as cancer immunotherapy, GLP-1 obesity, or CRISPR clinical trial.", "items": { "type": "string" } }, "pmids": { "title": "PMIDs", "type": "array", "description": "Direct PubMed IDs to summarize.", "items": { "type": "string" } }, "fromDate": { "title": "From Publication Date", "type": "string", "description": "Optional publication date floor in YYYY/MM/DD, YYYY/MM, or YYYY." }, "toDate": { "title": "To Publication Date", "type": "string", "description": "Optional publication date ceiling in YYYY/MM/DD, YYYY/MM, or YYYY." }, "sort": { "title": "Sort", "enum": [ "relevance", "pub_date", "most_recent" ], "type": "string", "description": "PubMed ESearch sort order.", "default": "relevance" }, "maxResultsPerQuery": { "title": "Max Results Per Query", "minimum": 1, "maximum": 200, "type": "integer", "description": "Maximum PMIDs to retrieve for each search term.", "default": 25 }, "maxArticles": { "title": "Max Articles", "minimum": 1, "maximum": 500, "type": "integer", "description": "Global unique article cap.", "default": 100 }, "email": { "title": "NCBI Contact Email", "type": "string", "description": "Optional contact email included with E-utilities requests." }, "apiKey": { "title": "NCBI API Key", "type": "string", "description": "Optional NCBI API key for higher rate limits. Leave empty for normal runs." }, "tool": { "title": "NCBI Tool Name", "type": "string", "description": "Tool name sent to NCBI E-utilities.", "default": "pubmed-research-intelligence" }, "timeoutMs": { "title": "Request Timeout (ms)", "minimum": 1000, "maximum": 60000, "type": "integer", "description": "Per-request timeout in milliseconds.", "default": 20000 }, "delivery": { "title": "Delivery", "enum": [ "dataset", "webhook" ], "type": "string", "description": "Send flattened rows to the dataset or POST the full payload to a webhook.", "default": "dataset" }, "webhookUrl": { "title": "Webhook URL", "type": "string", "description": "Required when delivery is webhook." }, "dryRun": { "title": "Dry Run", "type": "boolean", "description": "Run without saving dataset rows or sending a webhook.", "default": false } } }, "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 } } } } } } } } } } ```