Machine Learning Decision Support for Macronutrients in Neonatal Parenteral Nutrition
Parenteral nutrition plays a crucial role in the care of hospitalized patients in the neonatal intensive care unit. Determining the appropriate amount and composition of parenteral nutrition bag for each infant is a complex and individualized process. Using a dataset of 1210 neonatal intensive-care...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10752961/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850161952480821248 |
|---|---|
| author | Setareh Derakhshandara Valentina Franzoni Daniele Mezzetti Valentina Poggioni |
| author_facet | Setareh Derakhshandara Valentina Franzoni Daniele Mezzetti Valentina Poggioni |
| author_sort | Setareh Derakhshandara |
| collection | DOAJ |
| description | Parenteral nutrition plays a crucial role in the care of hospitalized patients in the neonatal intensive care unit. Determining the appropriate amount and composition of parenteral nutrition bag for each infant is a complex and individualized process. Using a dataset of 1210 neonatal intensive-care unit patients of the University Hospital of Perugia, collected over 17 years, this work aims to establish the basis for an evidence-based decision support system to reduce the time and effort required for manual calculations from doctors working in such a critical emergency environment. After the data was presented, we compared different machine learning techniques (i.e., random forest, gradient boosting machine, support vector machine, multilayer perceptron) that could predict nutritional requirements. We discuss the feasibility of the proposed approach, evaluating the methods in terms of their explainability, and using performance measures (i.e., RMSE, <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>). Preliminary results revealed promising predictive ability for macronutrients and volume of parenteral bags. These findings highlight the potential of machine learning as a valuable tool for nutritional outcome estimation in neonatal clinical practice. |
| format | Article |
| id | doaj-art-c65853f2eeed471ca075089fbf426074 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c65853f2eeed471ca075089fbf4260742025-08-20T02:22:40ZengIEEEIEEE Access2169-35362024-01-011217000117001010.1109/ACCESS.2024.349805910752961Machine Learning Decision Support for Macronutrients in Neonatal Parenteral NutritionSetareh Derakhshandara0Valentina Franzoni1https://orcid.org/0000-0002-2972-7188Daniele Mezzetti2https://orcid.org/0000-0001-7612-4382Valentina Poggioni3https://orcid.org/0000-0002-7691-7478Department of Mathematics and Computer Science, University of Perugia, Perugia, ItalyDepartment of Mathematics and Computer Science, University of Perugia, Perugia, ItalyNeonatal Intensive Care Unit, Santa Maria della Misericordia Hospital of Perugia, Perugia, ItalyDepartment of Mathematics and Computer Science, University of Perugia, Perugia, ItalyParenteral nutrition plays a crucial role in the care of hospitalized patients in the neonatal intensive care unit. Determining the appropriate amount and composition of parenteral nutrition bag for each infant is a complex and individualized process. Using a dataset of 1210 neonatal intensive-care unit patients of the University Hospital of Perugia, collected over 17 years, this work aims to establish the basis for an evidence-based decision support system to reduce the time and effort required for manual calculations from doctors working in such a critical emergency environment. After the data was presented, we compared different machine learning techniques (i.e., random forest, gradient boosting machine, support vector machine, multilayer perceptron) that could predict nutritional requirements. We discuss the feasibility of the proposed approach, evaluating the methods in terms of their explainability, and using performance measures (i.e., RMSE, <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>). Preliminary results revealed promising predictive ability for macronutrients and volume of parenteral bags. These findings highlight the potential of machine learning as a valuable tool for nutritional outcome estimation in neonatal clinical practice.https://ieeexplore.ieee.org/document/10752961/Machine learningintelligent decision systemsneonatal intensive careAI for decision in healthcaremachine learning for parenteral nutritioninfant nutrition |
| spellingShingle | Setareh Derakhshandara Valentina Franzoni Daniele Mezzetti Valentina Poggioni Machine Learning Decision Support for Macronutrients in Neonatal Parenteral Nutrition IEEE Access Machine learning intelligent decision systems neonatal intensive care AI for decision in healthcare machine learning for parenteral nutrition infant nutrition |
| title | Machine Learning Decision Support for Macronutrients in Neonatal Parenteral Nutrition |
| title_full | Machine Learning Decision Support for Macronutrients in Neonatal Parenteral Nutrition |
| title_fullStr | Machine Learning Decision Support for Macronutrients in Neonatal Parenteral Nutrition |
| title_full_unstemmed | Machine Learning Decision Support for Macronutrients in Neonatal Parenteral Nutrition |
| title_short | Machine Learning Decision Support for Macronutrients in Neonatal Parenteral Nutrition |
| title_sort | machine learning decision support for macronutrients in neonatal parenteral nutrition |
| topic | Machine learning intelligent decision systems neonatal intensive care AI for decision in healthcare machine learning for parenteral nutrition infant nutrition |
| url | https://ieeexplore.ieee.org/document/10752961/ |
| work_keys_str_mv | AT setarehderakhshandara machinelearningdecisionsupportformacronutrientsinneonatalparenteralnutrition AT valentinafranzoni machinelearningdecisionsupportformacronutrientsinneonatalparenteralnutrition AT danielemezzetti machinelearningdecisionsupportformacronutrientsinneonatalparenteralnutrition AT valentinapoggioni machinelearningdecisionsupportformacronutrientsinneonatalparenteralnutrition |