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clinical-decision-support

Generate publication-ready clinical decision support documents with GRADE and Kaplan-Meier analyses.

21|1|Updated Mar 19, 2026
Search Tags:#cohort-analysis#clinical-decision-support#cds-docs#biomarker-integration#grade-evidence#latex-pdf
One-click install
npx skills add https://github.com/OwnLabAI/ownlab --skill clinical-decision-support-ownlabai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clinical-decision-support
Source: https://github.com/OwnLabAI/ownlab/tree/main/mart/skills/scientific-skills/clinical-decision-support
Command: npx skills add https://github.com/OwnLabAI/ownlab --skill clinical-decision-support-ownlabai

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 professional clinical decision support (CDS) documents that unify population-level analyses, biomarker-driven stratification, and evidence-based guideline reporting, enabling rapid production of publication-ready outputs.

Core Features & Use Cases

  • Cohort analyses with biomarkers: Construct biomarker-stratified patient cohorts and summarize outcomes.
  • Treatment recommendations with GRADE: Produce evidence-based guidelines, including GRADE grading and decision pathways.
  • Publication-ready outputs: Generate LaTeX/PDF documents with integrated figures (Kaplan-Meier, forest plots, flowcharts) and tables suitable for regulatory submissions and scientific publications.

Quick Start

Describe your CDS document requirements in plain language and the skill will generate publication-ready LaTeX/PDF outputs with figures and tables.

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 clinical decision support documents with biomarker stratification and GRADE evidence grading?▼

Clinical decision support documents are generated by providing plain language requirements, which the skill transforms into publication-ready LaTeX/PDF outputs featuring biomarker-stratified cohorts, GRADE evidence grading, and Kaplan-Meier survival figures.

Can I use pandas and lifelines for cohort analysis and Kaplan-Meier survival curves in a clinical decision support workflow?▼

Yes, the skill leverages pandas and lifelines to construct biomarker-stratified patient cohorts and perform Kaplan-Meier survival analyses, summarizing population-level outcomes for clinical decision support documents.

What is GRADE evidence grading and how does it apply to treatment recommendations in clinical decision support?▼

GRADE evidence grading is a framework for rating the quality of evidence in clinical guidelines. This skill applies GRADE methodology to produce evidence-based treatment recommendations and decision pathways within its clinical decision support outputs.

Does this approach support LaTeX and TikZ flowcharts for regulatory submission documents?▼

Yes, the skill enforces LaTeX/PDF outputs and utilizes TikZ flowcharts to create publication-ready documents suitable for regulatory submissions and scientific publications in pharmaceutical research scenarios.

What's the best way to produce publication-ready clinical reports with forest plots and biomarker insights?▼

Producing publication-ready clinical reports is achieved by automating cohort analyses and biomarker integration, which yields LaTeX/PDF documents containing forest plots, Kaplan-Meier figures, and GRADE-graded guideline tables.

Do I need scikit-learn and scipy to integrate biomarker stratification into clinical decision support documents?▼

You need scipy and scikit-learn as dependencies to process population-level evidence, enabling the automated scripts to execute biomarker stratification and statistical analyses for your clinical decision support document generation.

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