clinical-decision-support

Generate publication-ready clinical decision support documents by providing cohort data and biomarker inputs to produce LaTeX/PDF outputs integrating subgroup comparisons, GRADE evidence grading, and Kaplan-Meier analysis. The Skill automates cohort analysis and formats the results into pharmaceutical-grade reports with TikZ decision flowcharts.

GRADE evidence grading systematically evaluates the quality and strength of clinical evidence to support treatment recommendations. This Skill applies GRADE methodology within clinical decision support reporting to ensure reproducible, evidence-based outcomes like OS, PFS, and ORR are accurately stratified and documented for pharmaceutical submissions.

Yes, the Skill uses lifelines and pandas to perform Kaplan-Meier survival analysis and cohort comparisons, then renders the statistical outputs directly into LaTeX/PDF reports. It bridges Python-based data analysis with scientific writing to create formatted, publication-ready clinical decision support documents.

Yes, the tool ensures HIPAA compliance and data de-identification for clinical research data by documenting data provenance and statistical methods. It processes cohort data to generate HIPAA-compliant clinical decision support reports, maintaining reproducibility and privacy standards required for pharmaceutical research.

The best way to create decision algorithm flowcharts for clinical guidelines is using TikZ-based rendering within LaTeX/PDF outputs. This Skill translates clinical decision algorithms into TikZ flowcharts, combining biomarker stratification and evidence grading to produce visual guideline schemas for treatment recommendations.

No, you do not need extensive prior LaTeX knowledge to output clinical decision support PDFs. You provide cohort data and biomarker inputs, and the Skill handles the scientific writing integration, automatically generating LaTeX/PDF templates with tables, figures, and TikZ flowcharts.