Dataset and Code for A Systems-Based Optimization Framework for Blenderized Enteral Nutrition: Integrating Physical Flow Constraints and Nutrient Delivery for Feeding Tube Calibers

This repository contains all computational materials, datasets, and reproducible scripts associated with the study “A Systems-Based Optimization Framework for Blenderized Enteral Nutrition: Integrating Physical Flow Constraints and Nutrient Delivery for Feeding Tube Calibers.”The work introduces a novel systems-based and multi-objective optimization (MOO) model designed to evaluate and optimize blenderized tube feeding (BTF) formulations by jointly considering nutrient density, dilution effects, viscosity-related flow constraints, and feeding tube caliber. The materials included in this Zenodo deposit enable full transparency, complete reproducibility, and independent verification of the analyses presented in the manuscript. The computational workflow integrates empirical bromatological nutrient measurements, simulation‑based estimates of viscosity and flow feasibility, and Pareto‑based optimization, producing all figures and decision-support outputs featured in the publication. Contents 1. Data EResultados_bromatologicos.xlsxContains the full bromatological nutrient analysis of blenderized preparations at 25%, 50%, 75%, and 100% dilutions across 4, 5, 8, and 14 French feeding tubes. Includes macronutrients, micronutrients, and energy values. 2. Code btf_system_model.pyA fully reproducible Python script that: Loads and preprocesses the experimental dataset. Computes nutrient means per dilution. Simulates viscosity–flow feasibility constraints. Generates multi-objective objective vectors. Computes Pareto frontiers for each tube caliber. Generates all figures used in the manuscript (heatmap, dilution curve, feasibility map, Pareto frontiers, and 3D trade‑off surface). 3. Figures heatmap_nutrients.pdf nutrient_vs_dilution.pdf pareto_frontiers.pdf flow_feasibility_heatmap.pdf tradeoff_surface.pdfAll figures are automatically generated from the Python model. 4. Documentation system_flowchart.pdfDiagram representing the systems-based computational framework used in the study. README file providing instructions for running the reproducibility script. Reproducibility This repository is fully reproducible.Running the following command regenerates all figures and intermediate computations:python btf_system_model.py