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scientific-skills/clinical-decision-support/README.md

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-# Clinical Decision Support Skill
-
-Professional clinical decision support documents for medical professionals in pharmaceutical and clinical research settings.
-
-## Quick Start
-
-This skill enables generation of three types of clinical documents:
-
-1. **Individual Patient Treatment Plans** - Personalized protocols for specific patients
-2. **Patient Cohort Analysis** - Biomarker-stratified group analyses with outcomes
-3. **Treatment Recommendation Reports** - Evidence-based clinical guidelines
-
-All documents are generated as compact, professional LaTeX/PDF files.
-
-## Directory Structure
-
-```
-clinical-decision-support/
-├── SKILL.md                     # Main skill definition
-├── README.md                    # This file
-│
-├── references/                  # Clinical guidance documents
-│   ├── patient_cohort_analysis.md
-│   ├── treatment_recommendations.md
-│   ├── clinical_decision_algorithms.md
-│   ├── biomarker_classification.md
-│   ├── outcome_analysis.md
-│   └── evidence_synthesis.md
-│
-├── assets/                      # Templates and examples
-│   ├── cohort_analysis_template.tex
-│   ├── treatment_recommendation_template.tex
-│   ├── clinical_pathway_template.tex
-│   ├── biomarker_report_template.tex
-│   ├── example_gbm_cohort.md
-│   ├── recommendation_strength_guide.md
-│   └── color_schemes.tex
-│
-└── scripts/                     # Analysis and generation tools
-    ├── generate_survival_analysis.py
-    ├── create_cohort_tables.py
-    ├── build_decision_tree.py
-    ├── biomarker_classifier.py
-    └── validate_cds_document.py
-```
-
-## Example Use Cases
-
-### Create a Patient Cohort Analysis
-```
-> Analyze a cohort of 45 NSCLC patients stratified by PD-L1 expression 
-  (<1%, 1-49%, ≥50%) including ORR, PFS, and OS outcomes
-```
-
-### Generate Treatment Recommendations
-```
-> Create evidence-based treatment recommendations for HER2-positive 
-  metastatic breast cancer with GRADE methodology
-```
-
-### Build Clinical Pathway
-```
-> Generate a clinical decision algorithm for acute chest pain 
-  management with TIMI risk score
-```
-
-## Key Features
-
-- **GRADE Methodology**: Evidence quality grading (High/Moderate/Low/Very Low)
-- **Recommendation Strength**: Strong (Grade 1) vs Conditional (Grade 2)
-- **Biomarker Integration**: Genomic, expression, and molecular subtype classification
-- **Statistical Analysis**: Kaplan-Meier, Cox regression, log-rank tests
-- **Guideline Concordance**: NCCN, ASCO, ESMO, AHA/ACC integration
-- **Professional Output**: 0.5in margins, color-coded boxes, publication-ready
-
-## Dependencies
-
-Python scripts require:
-- `pandas`, `numpy`, `scipy`: Data analysis and statistics
-- `lifelines`: Survival analysis (Kaplan-Meier, Cox regression)
-- `matplotlib`: Visualization
-- `pyyaml` (optional): YAML input for decision trees
-
-Install with:
-```bash
-pip install pandas numpy scipy lifelines matplotlib pyyaml
-```
-
-## References Included
-
-1. **Patient Cohort Analysis**: Stratification methods, biomarker correlations, statistical comparisons
-2. **Treatment Recommendations**: Evidence grading, treatment sequencing, special populations
-3. **Clinical Decision Algorithms**: Risk scores, decision trees, TikZ flowcharts
-4. **Biomarker Classification**: Genomic alterations, molecular subtypes, companion diagnostics
-5. **Outcome Analysis**: Survival methods, response criteria (RECIST), effect sizes
-6. **Evidence Synthesis**: Guideline integration, systematic reviews, meta-analysis
-
-## Templates Provided
-
-1. **Cohort Analysis**: Demographics table, biomarker profile, outcomes, statistics, recommendations
-2. **Treatment Recommendations**: Evidence review, GRADE-graded options, monitoring, decision algorithm
-3. **Clinical Pathway**: TikZ flowchart with risk stratification and urgency-coded actions
-4. **Biomarker Report**: Genomic profiling with tier-based actionability and therapy matching
-
-## Scripts Included
-
-1. **`generate_survival_analysis.py`**: Create Kaplan-Meier curves with hazard ratios
-2. **`create_cohort_tables.py`**: Generate baseline, efficacy, and safety tables
-3. **`build_decision_tree.py`**: Convert text/JSON to TikZ flowcharts
-4. **`biomarker_classifier.py`**: Stratify patients by PD-L1, HER2, molecular subtypes
-5. **`validate_cds_document.py`**: Quality checks for completeness and compliance
-
-## Integration
-
-Integrates with existing skills:
-- **scientific-writing**: Citation management, statistical reporting
-- **clinical-reports**: Medical terminology, HIPAA compliance
-- **scientific-schematics**: TikZ flowcharts
-
-## Version
-
-Version 1.0 - Initial release
-Created: November 2024
-Last Updated: November 5, 2024
-
-## Questions or Feedback
-
-This skill was designed for pharmaceutical and clinical research professionals creating clinical decision support documents. For questions about usage or suggestions for improvements, contact the Scientific Writer development team.
-

scientific-skills/clinical-decision-support/SKILL.md

@@ -2,6 +2,7 @@
 name: clinical-decision-support
 description: "Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). Supports GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance. Outputs publication-ready LaTeX/PDF format optimized for drug development, clinical research, and evidence synthesis."
 allowed-tools: [Read, Write, Edit, Bash]
+license: MIT License
 metadata:
     skill-author: K-Dense Inc.
 ---