clinical-decision-support

To generate a clinical decision support document, you input biomarker-stratified cohort data to produce publication-ready LaTeX/PDF outputs. The system automatically calculates survival statistics like Kaplan-Meier curves and hazard ratios, applies GRADE evidence grading, and formats regulatory-style treatment recommendations.

Yes, you can perform biomarker stratification for patient cohort analysis using Python with pandas and numpy. The workflow processes subgroup data to generate outcomes like OS, PFS, and ORR, outputting structured statistical reports with response and safety summaries.

Yes, the workflow produces LaTeX PDF reporting featuring TikZ flowcharts for treatment recommendation algorithms. It enforces the inclusion of scientific schematics, mapping biomarker decision pathways and clinical logic into publication-ready visual outputs.

The best way to automate regulatory-leaning documentation is by transforming cohort analyses and guideline logic into structured reports. This workflow enforces mandatory full-page executive summaries, applies GRADE frameworks, and integrates HIPAA de-identification guidance for clinical research contexts.

Yes, you need lifelines and scipy installed to calculate hazard ratios and log-rank tests for clinical decision support. These dependencies are required to perform the survival analysis and statistical outcome reporting on biomarker-stratified patient cohorts.