skills.rest
Zeyuyang-0420
Zeyuyang-0420COMMUNITY
Explore all skills by this author →
GitHub

clinical-decision-support

Generate clinical decision support documents with cohort analyses and treatment recommendations.

Updated May 8, 2026
Search Tags:#clinical decision support#evidence-based medicine#clinical guidelines#treatment recommendation#patient cohort analysis
One-click install
npx skills add https://github.com/Zeyuyang-0420/bio-ai-research-skills --skill clinical-decision-support-zeyuyang-0420
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clinical-decision-support
Source: https://github.com/Zeyuyang-0420/bio-ai-research-skills/tree/main/categories/clinical-biomedical-ai/clinical-decision-support
Command: npx skills add https://github.com/Zeyuyang-0420/bio-ai-research-skills --skill clinical-decision-support-zeyuyang-0420

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the generation of clinical decision support documents, significantly reducing the manual effort required to produce complex analytical and evidence-based documents.

Core Features & Use Cases

  • Patient Cohort Analysis: Generate comprehensive analyses of patient cohorts, including biomarker stratification and statistical outcomes.
  • Treatment Recommendation Reports: Create evidence-based clinical guidelines and treatment recommendation reports with GRADE evidence grading.
  • Use Case: For a clinical trial with 60 HER2-positive metastatic breast cancer patients, use this Skill to generate a treatment recommendation report including outcomes analysis, biomarker profiles, and GRADE-graded recommendations.

Quick Start

Use the clinical-decision-support skill to generate a patient cohort analysis for a cohort of 60 HER2-positive breast cancer patients treated with trastuzumab-deruxtecan, stratified by prior trastuzumab exposure (yes/no). Include ORR, DOR, median PFS with forest plot showing subgroup analyses by hormone receptor status, brain metastases, and number of prior lines.

Frequently Asked Questions about clinical-decision-support

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

FAQPage Schema
How do I automate clinical decision support document generation for patient cohort analysis?▼

Automating clinical decision support document generation requires Python libraries like pandas, numpy, scipy, and lifelines to perform patient cohort analysis, biomarker stratification, and statistical outcomes visualization. This process produces comprehensive analytical reports including forest plots for subgroup analyses.

Can I generate treatment recommendation reports with GRADE evidence grading using Python?▼

Generating treatment recommendation reports with GRADE evidence grading is supported through Python libraries including pandas, numpy, scipy, matplotlib, pyyaml, and lifelines. These enable evidence-based clinical guidelines creation with statistical analysis and biomarker profiling for pharmaceutical research workflows.

What is the best way to analyze HER2-positive breast cancer patient cohorts stratified by prior treatment exposure?▼

Analyzing HER2-positive breast cancer patient cohorts stratified by prior treatment exposure involves using lifelines for median PFS calculation, scipy for ORR and DOR statistical outcomes, and matplotlib for forest plot visualization of subgroup analyses by hormone receptor status and brain metastases.

Does this clinical decision support workflow require lifelines for survival analysis and forest plot generation?▼

Clinical decision support workflows requiring survival analysis and forest plot generation depend on lifelines for median PFS calculations and matplotlib for visualization. Additional dependencies include pandas for data manipulation, scipy for statistical testing, and pyyaml for configuration management.

How to create evidence-based clinical guidelines with biomarker stratification for metastatic breast cancer trials?▼

Creating evidence-based clinical guidelines with biomarker stratification for metastatic breast cancer trials utilizes pandas for cohort data processing, scipy for statistical significance testing, and lifelines for survival outcomes analysis. Results include GRADE-graded recommendations and biomarker profile visualizations.

What limitations exist when generating pharmaceutical research reports with Python statistical libraries?▼

Generating pharmaceutical research reports with Python statistical libraries requires proper installation of pandas, numpy, scipy, matplotlib, pyyaml, and lifelines. Limitations include dependency on data quality for accurate GRADE evidence grading and need for domain expertise to interpret biomarker stratification and forest plot subgroup analyses correctly.

Related Skills

Explore More Education & Research Skills→
liangdabiaoliangdabiao
264

retention-analysis

Diagnose churn risk and retention gaps in subscription data.

Community
Advanced
felixboehmfelixboehm

clinical-decision-support

Generate clinical decision support documents with biomarker-stratified cohort analyses and treatment recommendations.

Community
Advanced
jiaxiaojunQAQjiaxiaojunQAQ
75

clinical-decision-support

Generate publication-ready clinical decision support documents with GRADE evidence grading and statistical analyses.

Community
Advanced
Lord1EgyptLord1Egypt
2

clinical-decision-support

Generate clinical decision support documents with biomarker-stratified cohort analyses and GRADE evidence grading.

Community
Advanced