clinical-decision-support

Generate publication-ready clinical decision support documents by synthesizing evidence with GRADE-based quality grading, then outputting LaTeX/PDF reports with integrated assets, references, and reproducible methods.

Yes, you can create biomarker-stratified cohort analyses for oncology and other therapeutic areas, utilizing lifelines and pandas for survival analysis and population-level patient outcomes.

Produce treatment recommendation reports by applying GRADE methodology, trial evidence synthesis, and TikZ decision algorithms to format clinical guidelines into regulatory-ready documents.

Yes, the clinical decision support workflow supports de-identification and regulatory-ready formatting to ensure population-level analyses meet compliance standards for pharmaceutical research.

Standardize clinical guideline development by applying reusable LaTeX templates and integrated scripts to synthesize evidence, producing consistent, publication-ready CDS documents.

Yes, you need pandas, numpy, scipy, lifelines, and matplotlib installed to execute the scripts for survival analysis and generate matplotlib visualizations for clinical decision support.