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clinical-decision-support

Generate biomarker-stratified cohort analyses and GRADE-graded treatment recommendation reports as LaTeX/PDF documents.

Updated Jun 18, 2026
Search Tags:#survival analysis#latex#cohort analysis#clinical decision support#biomarker#treatment guidelines#grade evidence
One-click install
npx skills add https://github.com/svpfahad/RES200 --skill clinical-decision-support-svpfahad
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clinical-decision-support
Source: https://github.com/svpfahad/RES200/tree/main/claude-scientific-writer-main/.claude/skills/clinical-decision-support
Command: npx skills add https://github.com/svpfahad/RES200 --skill clinical-decision-support-svpfahad

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? Pharmaceutical researchers and clinical guideline developers need rigorous, publication-ready documents that synthesize biomarker-stratified cohort outcomes and evidence-graded treatment recommendations, which are time-consuming to produce manually with proper statistics and formatting. ## Core Features & Use Cases - Patient Cohort Analysis: Stratify cohorts by biomarkers (PD-L1, HER2, molecular subtypes) and report ORR, PFS, OS with hazard ratios, Kaplan-Meier curves, and waterfall plots. - Treatment Recommendation Reports: Build evidence-based guidelines with GRADE grading (1A-2C), TikZ decision algorithm flowcharts, and line-of-therapy sequencing. - Statistical & Compliance Tooling: Python scripts for survival analysis, cohort tables, biomarker classification, and HIPAA de-identification checks. - Use Case: Analyze 45 NSCLC patients stratified by PD-L1 expression receiving pembrolizumab, producing a compact LaTeX/PDF report with survival curves, subgroup comparisons, and a one-page executive summary. ## Quick Start Ask the AI to analyze a cohort of NSCLC patients stratified by PD-L1 expression with ORR, PFS, and OS outcomes and generate a publication-ready PDF report.

Frequently Asked Questions about clinical-decision-support

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

FAQPage Schema
How do I create a biomarker-stratified patient cohort analysis?▼

Provide patient data with biomarker values such as PD-L1 TPS or HER2 status, and the skill stratifies the cohort, computes outcomes like ORR, PFS, and OS, and generates Kaplan-Meier curves with hazard ratios. Output is a compact LaTeX/PDF report with an executive summary page.

How do I generate treatment recommendations with GRADE evidence grading?▼

Specify the disease state, target population, and lines of therapy, and the skill produces a recommendation report grading each option as 1A through 2C with supporting trial evidence. It also builds TikZ flowcharts showing biomarker-guided decision pathways.

What Python dependencies are required for the analysis scripts?▼

The scripts require pandas, numpy, and scipy for data analysis, lifelines for Kaplan-Meier and Cox regression survival analysis, matplotlib for visualization, and optionally pyyaml for decision tree input. Install them with pip before running the scripts.

Can this skill create individual patient treatment plans?▼

No, this skill is designed for population-level cohort analyses and guideline documents for pharmaceutical and research settings. For individual bedside patient treatment plans, the separate treatment-plans skill should be used instead.

Does the skill support HER2 and PD-L1 classification standards?▼

Yes, the biomarker_classifier.py script implements ASCO/CAP HER2 scoring with IHC and FISH reflex logic, and PD-L1 TPS categories of negative, low 1-49%, and high 50% or above. It also supports breast cancer molecular subtyping and TMB/MSI interpretation.

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