clinical-decision-support

To generate clinical decision support reports from biomarker data, you provide a structured dataset to stratify patient cohorts and the workflow outputs publication-ready LaTeX/PDF documents with figures and tables.

Yes, lifelines is supported for Kaplan-Meier survival analysis to compare outcomes across biomarker-stratified cohorts, producing subgroup forest plots and hazard ratios for clinical decision support documents.

The best way to apply GRADE evidence grading for treatment guidelines is through systematic evidence synthesis, which evaluates biomarker data and generates TikZ flowcharts and clinical algorithms for regulatory submissions.

Yes, LaTeX and TikZ are required to produce publication-ready clinical guideline documents, as the workflow renders decision algorithms and formatted tables directly into LaTeX/PDF templates for manuscript submission.

Yes, the clinical decision support workflow handles cohort analysis for cardiology and neurology, alongside oncology, delivering population-level biomarker-supported stratification and outcome comparisons for these therapeutic areas.

The workflow uses pandas, scipy, and scikit-learn for data processing but requires pre-structured biomarker inputs and systematic evidence synthesis to function, meaning unstructured clinical data cannot be processed without prior formatting.