A Transparent Rule-Based Decision-Support Prototype for Diabetic Menu Planning and Meal-Combination Optimization: Development and Computational Evaluation.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	A Transparent Rule-Based Decision-Support Prototype for Diabetic Menu Planning and Meal-Combination Optimization: Development and Computational Evaluation.
Συγγραφείς:	Marić M; Nutricionističko Savjetovalište za Prehranu dr. sc. Mirela Marić, Petrova 47, 10000 Zagreb, Croatia., Knights VA; Faculty of Technology and Technical Sciences, University 'St. Kliment Ohridski'-Bitola, Dimitar Vlahov 57, 1400 Veles, North Macedonia., Bytyqi D; Faculty of Technology and Technical Sciences, University 'St. Kliment Ohridski'-Bitola, Dimitar Vlahov 57, 1400 Veles, North Macedonia., Jankuloska V; Faculty of Technology and Technical Sciences, University 'St. Kliment Ohridski'-Bitola, Dimitar Vlahov 57, 1400 Veles, North Macedonia., Gajdoš Kljusurić J; Faculty of Food Technology and Biotechnology, University of Zagreb, Pierottijeva 6, 10000 Zagreb, Croatia.
Πηγή:	Nutrients [Nutrients] 2026 Sep 23; Vol. 18 (19). Date of Electronic Publication: 2026 Sep 23.
Τύπος έκδοσης:	Journal Article
Γλώσσα:	English
Στοιχεία περιοδικού:	Publisher: MDPI Publishing Country of Publication: Switzerland NLM ID: 101521595 Publication Model: Electronic Cited Medium: Internet ISSN: 2072-6643 (Electronic) Linking ISSN: 20726643 NLM ISO Abbreviation: Nutrients Subsets: MEDLINE
Imprint Name(s):	Original Publication: Basel, Switzerland : MDPI Publishing
Ιατρικοί όροι (MeSH):	Menu Planning/methods , Diabetes Mellitus/diet therapy , Diet, Diabetic/methods , Meals , Decision Support Techniques*, Humans ; Programming, Linear ; Energy Intake
Περίληψη:	Background/Objectives: Translating medical nutrition therapy guidance into practical meal plans requires consistent application of clinical and dietetic rules while accommodating different therapeutic regimens. This study developed and computationally evaluated a transparent Python-based decision-support prototype for diabetic menu planning. Methods: The prototype combines procedural decision rules, food-exchange patterns, nutritional checks, and mixed-integer linear programming (MILP). Two meal-frequency pathways were represented: a conventional multi-meal pathway and a three-main-meal pathway for people treated with modern basal-bolus insulin regimens. For the multi-meal pathway, a structured dataset containing 42 candidate meals (seven alternatives in each of six meal categories) was used for objective-specific optimization and exhaustive evaluation of all 117,649 possible daily combinations under predefined nutritional constraints. Sensitivity to the allowable energy deviation and trade-offs among nutritional objectives were also examined. Results: The prototype produced deterministic pathway selection, exchange allocation, nutritional summaries, and explicit warnings when required information was unavailable. Under the baseline ±6.5% energy tolerance, 5628 of 117,649 six-meal combinations (4.78%) satisfied all nutritional constraints. Objective-specific optimization identified feasible solutions for minimizing energy, carbohydrate, and saturated fat and maximizing dietary fiber. The proportion of feasible combinations ranged from 2.71% at ±5% energy tolerance to 9.47% at ±10%, while comparison of objective-specific solutions demonstrated measurable nutritional trade-offs. The three-meal basal-bolus pathway generated predefined exchange allocation and clinical-review warnings but was not subjected to the same exhaustive combinatorial optimization. Conclusions: The prototype demonstrates the computational feasibility of integrating explicit dietetic rules, regimen-related meal-pattern selection, food-exchange allocation, and mathematical optimization within an inspectable decision-support framework. The findings establish technical feasibility within the evaluated datasets rather than clinical effectiveness. Independent software testing, expert usability assessment, evaluation using larger and version-controlled meal datasets, and prospective clinical validation are required before routine clinical implementation.
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Contributed Indexing:	Keywords: clinical nutrition; decision support systems; diabetes mellitus; diet optimization; medical nutrition therapy; menu planning; mixed-integer linear programming; rule-based systems
Entry Date(s):	Date Created: 20261016 Date Completed: 20261016 Latest Revision: 20261020
Update Code:	20261020
PubMed Central ID:	PMC13666910
DOI:	10.3390/nu18193140
PMID:	42884591
Βάση Δεδομένων:	MEDLINE

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ISSN:	2072-6643
DOI:	10.3390/nu18193140