Source: https://github.com/aipoch/medical-research-skills

Research Hotspot Analysis

When to Use

• Use this skill when you need analyzes research hotspots and recommends literature based on a disease or topic. use when the user wants to identify current research trends, hot topics, or get literature recommendations for a specific medical field or disease in a reproducible workflow.
• Use this skill when a evidence insight task needs a packaged method instead of ad-hoc freeform output.
• Use this skill when the user expects a concrete deliverable, validation step, or file-based result.
• Use this skill when

  scripts/analysis_ops.py

  is the most direct path to complete the request.
• Use this skill when you need the

  research-hotspot-analysis

  package behavior rather than a generic answer.

Key Features

• Scope-focused workflow aligned to: Analyzes research hotspots and recommends literature based on a disease or topic. Use when the user wants to identify current research trends, hot topics, or get literature recommendations for a specific medical field or disease.
• Packaged executable path(s):

  scripts/analysis_ops.py

  .
• Reference material available in

  references/

  for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python

  :

  3.10+

  . Repository baseline for current packaged skills.

• Third-party packages

  :

  not explicitly version-pinned in this skill package

  . Add pinned versions if this skill needs stricter environment control.

Example Usage

See

## Usage

above for related details.

cd "20260316/scientific-skills/Evidence Insight/research-hotspot-analysis"
python -m py_compile scripts/analysis_ops.py
python scripts/analysis_ops.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file

   CONFIG

   block or documented parameters if the script uses fixed settings.
3. Run

   python scripts/analysis_ops.py

   with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See

## Workflow

above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface:

  scripts/analysis_ops.py

  .
• Reference guidance:

  references/

  contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Description

This skill analyzes research hotspots for a given disease or topic by searching recent literature, calculating keyword frequencies, clustering topics, and recommending high-impact papers.

Usage

1. Input: The user provides a disease name or research topic (e.g., "Lung Cancer", "Diabetes").
2. Process:
   • Searches for recent literature (PMIDs) using the internal literature database.
   • Analyzes MESH terms to calculate word frequency and identify top keywords.
   • Uses LLM to cluster keywords into "Hotspot Topics".
   • Matches specific PMIDs to each topic.
   • Fetches full details (PMC) for top-ranked papers (by JIF/Availability).
   • Generates a comprehensive report with an introduction and detailed hotspot analysis.
3. Output: A Markdown report containing the research overview and specific paper recommendations per hotspot.

Workflow

1. Search Literature: Use

   scripts/analysis_ops.py

   (

   search_pubmed

   ) to find relevant PMIDs and fetch details.
2. Analyze Keywords: Use

   scripts/analysis_ops.py

   (

   word_frequency

   ) on the

   medline_texts

   output from Step 1 to find top MESH terms.
3. Identify Topics: Use LLM with

   references/prompt_templates.md

   (Hotspot Analysis) to group keywords into topics.
4. Match Evidence: Use

   scripts/analysis_ops.py

   (

   match_keywords

   ) with

   documents

   from Step 1 to map PMIDs to topics.
5. Fetch Details: For each topic, select top papers using

   scripts/analysis_ops.py

   (

   sort_by_jif_and_select

   ) and fetch details using

   fetchPMCArticleDetails

   .
6. Generate Report: Synthesize the findings into a final report using LLM.

Tools

• fetchPMCArticleDetails

  : Get article details.

• fetchPubmedArticleDetails

  : Get PubMed details.

Scripts

• scripts/analysis_ops.py

  : Contains helper functions for PubMed search, frequency analysis, keyword matching, and result formatting.

References

• references/prompt_templates.md

  : Contains the system prompts for LLM analysis.