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clinical-decision-support

Name: clinical-decision-support
Availability: InStock
Author: estrella-231

Generate LaTeX/PDF clinical decision support documents with biomarker-stratified cohort analyses and GRADE-graded recommendations.

Updated May 24, 2026

Search Tags:#survival analysis #clinical decision support #biomarker stratification #GRADE evidence grading #LaTeX PDF #clinical guideline algorithms

Ready to run

npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill clinical-decision-support-estrella-231

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: clinical-decision-support
Source: https://github.com/Estrella-231/Mathematical_modeling_tongmeng/tree/main/.agents/skills/clinical-decision-support
Command: npx skills add https://github.com/Estrella-231/Mathematical_modeling_tongmeng --skill clinical-decision-support-estrella-231

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

It helps pharmaceutical teams, clinical researchers, and guideline developers produce high-quality clinical decision support documents that synthesize biomarker-stratified cohort evidence and evidence-graded treatment recommendations.

Core Features & Use Cases

Cohort & biomarker analysis writeups: Generate patient-cohort narratives stratified by biomarkers (e.g., PD-L1, HER2, molecular subtype) with outcome reporting (OS/PFS/ORR) and statistical summaries (hazard ratios, p-values, survival analysis).
Evidence-graded treatment recommendations: Produce guideline-style recommendations using GRADE evidence grading and decision algorithms aligned to regulatory-style reporting.
Publication-ready LaTeX/PDF deliverables: Output compact, professional LaTeX structures suitable for manuscripts, pharma documentation, and evidence synthesis; includes mandatory executive summary formatting and figures/diagrams.

Quick Start

Use the clinical-decision-support skill to create a biomarker-stratified patient cohort analysis PDF for 60 HER2-positive metastatic breast cancer patients, including OS/PFS comparisons, GRADE-based recommendation implications, and a decision-flow diagram schematic.

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

To generate a clinical decision support PDF, this skill synthesizes biomarker-stratified patient cohort analyses and applies GRADE evidence grading to treatment recommendations, outputting a publication-ready LaTeX document with an executive summary and clinical schematics.

Can I create survival analysis curves and hazard ratio plots for patient cohort reporting using Python?▼

Yes, you can generate patient cohort reporting with survival analysis by utilizing built-in libraries like lifelines and matplotlib to calculate hazard ratios and render survival curves, which are then embedded into the final clinical decision support PDF.

Does this clinical decision support skill work with biomarker data like PD-L1 or HER2 for outcome reporting?▼

Yes, this skill processes biomarker data like PD-L1 or HER2 to generate patient cohort narratives stratified by molecular subtype, providing statistical outcome reporting including OS, PFS, and ORR metrics for clinical research settings.

What is the best way to structure a guideline-style treatment recommendation report with decision algorithms?▼

The best way to structure guideline-style treatment recommendations is by using the mandatory LaTeX format, which places an executive summary on page one and follows with GRADE-graded decision algorithms and generated clinical schematics to support visual decision pathways.

Do I need LaTeX installed to output the clinical decision support documents and schematics?▼

Yes, a LaTeX environment is required to compile the mandatory publication-ready PDF structure, which includes the executive summary on page one, generated clinical schematics, and figures supporting the decision pathways and visual clarity.