clinical-decision-support

Generate publication-ready CDS documents from biomarker-driven data in LaTeX/PDF.

1|Updated Apr 8, 2026

Search Tags:#pdf #latex #document-generation #biomarkers #clinical-decision-support #grade-evidence

Ready to run

npx skills add https://github.com/brainworkup/skills --skill clinical-decision-support-brainworkup

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: clinical-decision-support
Source: https://github.com/brainworkup/skills/tree/main/neuropsych-reports/references/luria-related-complement-skills/clinical-decision-support
Command: npx skills add https://github.com/brainworkup/skills --skill clinical-decision-support-brainworkup

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including population analyses, evidence grading, and decision algorithms, in publication-ready LaTeX/PDF formats.

Core Features & Use Cases

Comprehensive CDS document generation: cohort analyses by biomarkers, treatment recommendations with GRADE grading, and regulatory-ready reporting.
Template-driven production: LaTeX/PDF outputs using asset templates, with sections for executive summaries, methods, results, and figures.
Lifecycle workflows: supports biomarker integration, statistical analyses, and decision algorithms for population-level decision-making.

Quick Start

Generate a publication-ready CDS document for a biomarker-stratified patient cohort.

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 documents from biomarker data?▼

You can generate publication-ready clinical decision support documents by using modular LaTeX templates and script-based generation tools to transform biomarker-driven data into formatted PDF outputs with cohort analyses and GRADE-based recommendations.

What is GRADE-based evidence grading and how does it apply to clinical decision support?▼

GRADE-based evidence grading evaluates the quality of clinical evidence to support treatment recommendations. It applies to clinical decision support by integrating biomarker-driven statistical analyses to produce standardized, regulatory-ready decision algorithms for population-level care.

Can I use pandas and scipy for biomarker stratification in clinical cohort analyses?▼

Yes, you can use pandas and scipy for biomarker stratification in clinical cohort analyses. These dependencies support statistical analysis workflows that feed into LaTeX/PDF document generation for comprehensive population-level decision reporting.

Does this clinical decision support workflow output LaTeX and PDF formats for regulatory submission?▼

Yes, the clinical decision support workflow outputs LaTeX and PDF formats designed for regulatory readiness. It uses asset templates to enforce reproducibility and confidentiality while producing executive summaries, methods, results, and figures.

What's the best way to automate cohort analysis reporting for pharmaceutical clinical research?▼

The best way to automate cohort analysis reporting is through template-driven production using frontmatter metadata and script-based generation tools. This approach enforces reproducibility while creating standardized LaTeX/PDF documents from biomarker-stratified patient data.

When should I not use automated LaTeX document generation for clinical decision support?▼

You should avoid automated LaTeX document generation for clinical decision support when your workflow lacks biomarker-driven data or GRADE evidence requirements. The process is designed for pharmaceutical research contexts requiring cohort analyses, statistical modeling, and regulatory-ready reporting.