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clinical-decision-support

Generate publication-ready clinical decision support documents from biomarker-driven cohort data.

15|2|Updated Dec 17, 2025
Search Tags:#LaTeX#cohort-analysis#evidence-synthesis#GRADE#clinical-decision-support
One-click install
npx skills add https://github.com/rubensliv/k-dense-ai --skill clinical-decision-support-rubensliv
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clinical-decision-support
Source: https://github.com/rubensliv/k-dense-ai/tree/main/scientific-skills/clinical-decision-support
Command: npx skills add https://github.com/rubensliv/k-dense-ai --skill clinical-decision-support-rubensliv

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, lifelines, matplotlib, pyyaml, scikit-learn, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Generate standardized, publication-ready clinical decision support (CDS) documents from biomarker-driven data to streamline evidence synthesis, guideline development, and regulatory submissions.

Core Features & Use Cases

  • Biomarker-stratified cohort analyses with survival outcomes (OS, PFS), waterfall and forest plots, and Kaplan-Meier curves
  • Evidence-based treatment recommendation reports with GRADE grading and decision algorithms
  • Publication-ready LaTeX/PDF templates for cohort analyses, CDS reports, clinical pathways, and biomarker reports
  • Integrated references, templates, and schematics to support clinical decision pathways and regulatory documentation

Quick Start

Generate CDS documents from a biomarker-driven dataset to produce publication-ready LaTeX/PDF reports

Frequently Asked Questions about clinical-decision-support

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate publication-ready clinical decision support reports from biomarker-driven cohort data?▼

To generate publication-ready clinical decision support reports from biomarker-driven cohort data, you can automate the process using this Skill to produce LaTeX/PDF documents with GRADE evidence grading and Kaplan-Meier curves.

Can I perform Kaplan-Meier survival analysis and generate forest plots for cohort analysis?▼

Yes, you can perform Kaplan-Meier survival analysis and generate forest plots for cohort analysis. The Skill utilizes lifelines and matplotlib to visualize survival outcomes like OS and PFS for biomarker-stratified populations.

How do I apply GRADE evidence synthesis grading to clinical pathway documents?▼

You can apply GRADE evidence synthesis grading to clinical pathway documents by using the integrated templates and schematics. This ensures standardized, evidence-based treatment recommendations within the generated reports.

Does this tool require pandas and lifelines dependencies for evidence synthesis?▼

Yes, this tool requires pandas and lifelines dependencies for evidence synthesis, alongside numpy, scipy, and scikit-learn, to properly process biomarker-driven data and compute statistical survival outcomes.

What is the best way to automate LaTeX reporting for regulatory submissions?▼

The best way to automate LaTeX reporting for regulatory submissions is to use the built-in TikZ/LaTeX templates. They convert analyzed cohort data and GRADE assessments directly into standardized publication-ready PDFs.

Are there limitations when using matplotlib and pyyaml for biomarker-stratified cohort analyses?▼

Limitations when using matplotlib and pyyaml for biomarker-stratified cohort analyses depend on your local Python environment setup. The Skill provides standardized scripts and templates but requires proper dependency configuration to function.

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