# PubMed Research Paper Intelligence (`wakey7dev/pubmed-research-normalizer`) Actor

Search PubMed for biomedical literature — titles, abstracts, authors, MeSH terms. 36M+ citations. Pharma research, healthcare, academic literature reviews.

- **URL**: https://apify.com/wakey7dev/pubmed-research-normalizer.md
- **Developed by:** [Chris Wakefield](https://apify.com/wakey7dev) (community)
- **Categories:** AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 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/actors/running/actors-in-store.md#pay-per-usage

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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

![Chris The Dev](https://raw.githubusercontent.com/chriswakefield87/appstore-screenshot-translator/main/assets/actor-banner.png)

## 🧬 PubMed Research Normalizer

Search 37M+ biomedical papers from PubMed with **smart normalization** — drug names mapped to generics, authors deduplicated, MeSH terms categorized, institutions cleaned. Built on the free NCBI E-utilities API.

### ✨ What makes this different?

Most PubMed scrapers just dump raw API output. This actor **enriches and normalizes**:

- 💊 **Drug name normalization**: 150+ brand names → generic names (Keytruda → Pembrolizumab)
- 👥 **Author deduplication**: "Smith JA" and "Smith J" normalized to "Smith, J."
- 🏥 **Institution cleaning**: "Harvard Med School" → "Harvard Medical School"
- 🏷️ **MeSH categorization**: Raw MeSH terms grouped into clinical categories (Oncology, Cardiology, etc.)
- 📊 **Human-readable output**: Formatted summary table in the run log

### 📥 Input

| Field | Type | Required | Description |
|---|---|---|---|
| `searchQuery` | string | ✅ | PubMed search query. Supports field tags: `[tiab]`, `[au]`, `[dp]`, `[mh]` |
| `maxResults` | integer | ❌ | Max papers to return (1–500). Default: 20 |
| `sortBy` | string | ❌ | `relevance`, `date`, or `pub_date`. Default: `relevance` |
| `dateFrom` | string | ❌ | Earliest date (YYYY/MM/DD or YYYY) |
| `dateTo` | string | ❌ | Latest date (YYYY/MM/DD or YYYY) |
| `includeAbstracts` | boolean | ❌ | Fetch full abstracts. Default: true |
| `normalizeDrugNames` | boolean | ❌ | Map brand→generic drug names. Default: true |
| `normalizeAuthors` | boolean | ❌ | Deduplicate author names. Default: true |

#### Example Input

```json
{
  "searchQuery": "pembrolizumab melanoma[tiab] AND 2025[dp]",
  "maxResults": 25,
  "sortBy": "pub_date",
  "includeAbstracts": true,
  "normalizeDrugNames": true
}
```

### 📤 Output

Each paper is returned as a normalized JSON object:

```json
{
  "pmid": "42560690",
  "doi": "10.1001/jamaoto.2026.2124",
  "title": "Combined Positive Score and Cost-Effectiveness of Perioperative Pembrolizumab...",
  "journal": "JAMA Otolaryngology--Head & Neck Surgery",
  "publicationDate": "2026 Aug 6",
  "authors": ["Coyle, A.H.", "Hutton, D.W.", "Buchakjian, M.R.", ...],
  "authorCount": 17,
  "drugMentions": ["Pembrolizumab"],
  "meshCategories": ["Oncology", "Immunotherapy", "Health Policy"],
  "abstract": "Importance: Head and neck cancer...",
  "pubmedUrl": "https://pubmed.ncbi.nlm.nih.gov/42560690/",
  "doiUrl": "https://doi.org/10.1001/jamaoto.2026.2124"
}
```

#### Output Schema

Three endpoints for different consumers:

- **Dataset**: Full paper records as JSON array
- **OUTPUT (KVS)**: Human-readable formatted table
- **STATS (KVS)**: Machine-readable run statistics

### 🎯 Use Cases

- **Pharma competitive intelligence**: Track competitor drug publications
- **Systematic reviews**: Search and normalize papers for meta-analysis
- **Medical affairs**: Monitor literature for key drugs and conditions
- **Biotech R\&D**: Identify emerging research trends
- **AI/ML training data**: Curated biomedical text corpus

### 🔌 Data Source

[NCBI PubMed](https://pubmed.ncbi.nlm.nih.gov/) via the [E-utilities API](https://www.ncbi.nlm.nih.gov/books/NBK25501/). Free, no API key required. Rate limit: ~3 requests/second.

### 💰 Pricing

Pay per result — only charged when papers are returned. Empty searches are free.

***

Built by [Chris The Dev](https://apify.com/wakey7dev)

# Actor input Schema

## `searchQuery` (type: `string`):

PubMed search query. Supports field tags like \[tiab], \[au], \[dp], \[mh]. Example: 'cancer immunotherapy\[tiab] AND 2024\[dp]'

## `maxResults` (type: `integer`):

Maximum number of papers to return (1-500). Default: 20.

## `sortBy` (type: `string`):

Sort order for results.

## `dateFrom` (type: `string`):

Earliest publication date (YYYY/MM/DD or YYYY). Leave empty for no filter.

## `dateTo` (type: `string`):

Latest publication date (YYYY/MM/DD or YYYY). Leave empty for no filter.

## `includeAbstracts` (type: `boolean`):

Fetch full abstracts via EFetch (adds ~1s per paper). Default: true.

## `normalizeDrugNames` (type: `boolean`):

Map brand names to generics in titles/abstracts. Default: true.

## `normalizeAuthors` (type: `boolean`):

Deduplicate and normalize author names. Default: true.

## Actor input object example

```json
{
  "searchQuery": "cancer immunotherapy",
  "maxResults": 20,
  "sortBy": "relevance",
  "includeAbstracts": true,
  "normalizeDrugNames": true,
  "normalizeAuthors": true
}
```

# Actor output Schema

## `results` (type: `string`):

All normalized paper records as JSON array.

## `summary` (type: `string`):

Human-readable formatted summary of results.

## `stats` (type: `string`):

Machine-readable run statistics.

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "searchQuery": "cancer immunotherapy"
};

// Run the Actor and wait for it to finish
const run = await client.actor("wakey7dev/pubmed-research-normalizer").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "searchQuery": "cancer immunotherapy" }

# Run the Actor and wait for it to finish
run = client.actor("wakey7dev/pubmed-research-normalizer").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "searchQuery": "cancer immunotherapy"
}' |
apify call wakey7dev/pubmed-research-normalizer --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,wakey7dev/pubmed-research-normalizer"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/AyEfhqDWI8YmZFfIc/builds/1dA2UksEtbt3juhcr/openapi.json