{"slug":"alterlab-clinical-decision","title":"alterlab-clinical-decision","summary":"Generates professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings — biomarker-stratified patient cohort analyses with outcomes and evidence-based treatment recommendation reports with decision algorithms, supporting GRADE evidence ","platform":"Claude","tags":[],"authorName":"LLM Mart","authorSlug":"llm-mart","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-09-23T18:57:00.300389Z","repo":{"url":"https://github.com/AlterLab-IEU/AlterLab-Academic-Skills","stars":68,"forks":13,"license":"MIT","updatedAt":"2026-09-23T13:42:59Z"},"bodyHtml":"

Clinical Decision Support Skill

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Professional clinical decision support documents for medical professionals in pharmaceutical and clinical research settings.

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Quick Start

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This skill generates two types of group-level clinical documents:

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  1. Patient Cohort Analysis - Biomarker-stratified group analyses with outcomes
    2. \n
  2. Treatment Recommendation Reports - Evidence-based clinical guidelines with GRADE/ACCP grading
    3. \n
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All documents are generated as compact, professional LaTeX/PDF files.

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For individual patient treatment plans use alterlab-treatment-plans; for single-patient case reports use alterlab-clinical-reports.

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Directory Structure

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alterlab-clinical-decision/\n├── SKILL.md                     # Main skill definition\n├── README.md                    # This file\n│\n├── references/                  # Clinical guidance documents\n│   ├── patient_cohort_analysis.md\n│   ├── treatment_recommendations.md\n│   ├── clinical_decision_algorithms.md\n│   ├── biomarker_classification.md\n│   ├── outcome_analysis.md\n│   └── evidence_synthesis.md\n│\n├── assets/                      # Templates and examples\n│   ├── cohort_analysis_template.tex\n│   ├── treatment_recommendation_template.tex\n│   ├── clinical_pathway_template.tex\n│   ├── biomarker_report_template.tex\n│   ├── example_gbm_cohort.md\n│   ├── recommendation_strength_guide.md\n│   └── color_schemes.tex\n│\n└── scripts/                     # Analysis and generation tools\n    ├── generate_survival_analysis.py\n    ├── create_cohort_tables.py\n    ├── build_decision_tree.py\n    ├── biomarker_classifier.py\n    └── validate_cds_document.py\n
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Example Use Cases

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Create a Patient Cohort Analysis

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> Analyze a cohort of 45 NSCLC patients stratified by PD-L1 expression \n  (<1%, 1-49%, ≥50%) including ORR, PFS, and OS outcomes\n
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Generate Treatment Recommendations

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> Create evidence-based treatment recommendations for HER2-positive \n  metastatic breast cancer with GRADE methodology\n
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Build Clinical Pathway

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> Generate a clinical decision algorithm for acute chest pain \n  management with TIMI risk score\n
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Key Features

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Dependencies

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Python scripts require:

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Install with uv (preferred) or pip:

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uv pip install pandas numpy scipy lifelines matplotlib pyyaml\n
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References Included

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  1. Patient Cohort Analysis: Stratification methods, biomarker correlations, statistical comparisons
    2. \n
  2. Treatment Recommendations: Evidence grading, treatment sequencing, special populations
    3. \n
  3. Clinical Decision Algorithms: Risk scores, decision trees, TikZ flowcharts
    4. \n
  4. Biomarker Classification: Genomic alterations, molecular subtypes, companion diagnostics
    5. \n
  5. Outcome Analysis: Survival methods, response criteria (RECIST), effect sizes
    6. \n
  6. Evidence Synthesis: Guideline integration, systematic reviews, meta-analysis
    7. \n
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Templates Provided

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  1. Cohort Analysis: Demographics table, biomarker profile, outcomes, statistics, recommendations
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  2. Treatment Recommendations: Evidence review, GRADE-graded options, monitoring, decision algorithm
    3. \n
  3. Clinical Pathway: TikZ flowchart with risk stratification and urgency-coded actions
    4. \n
  4. Biomarker Report: Genomic profiling with tier-based actionability and therapy matching
    5. \n
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Scripts Included

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  1. generate_survival_analysis.py: Create Kaplan-Meier curves with hazard ratios
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  2. create_cohort_tables.py: Generate baseline, efficacy, and safety tables
    3. \n
  3. build_decision_tree.py: Convert text/JSON to TikZ flowcharts
    4. \n
  4. biomarker_classifier.py: Stratify patients by PD-L1, HER2, molecular subtypes
    5. \n
  5. validate_cds_document.py: Quality checks for completeness and compliance
    6. \n
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Integration

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Integrates with sibling skills:

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Version is tracked in SKILL.md frontmatter (metadata.version).

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