An Ontology and Rule-Based Clinical Decision Support System for Personalized Nutrition Recommendations in the Neonatal Intensive Care Unit
Premature neonates hospitalized in the neonatal intensive care unit (NICU) have high nutritional needs to ensure optimal growth and development. Monitoring adherence to neonatal nutritional guidelines and providing personalized nutritional recommendations are essential for promoting their health. Ho...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10352145/ |
| _version_ | 1797376346516619264 |
|---|---|
| author | Ravneet Kaur Monika Jain Ryan M. McAdams Yao Sun Shubham Gupta Raghava Mutharaju Su Jin Cho Satish Saluja Jonathan P. Palma Avneet Kaur Harpreet Singh |
| author_facet | Ravneet Kaur Monika Jain Ryan M. McAdams Yao Sun Shubham Gupta Raghava Mutharaju Su Jin Cho Satish Saluja Jonathan P. Palma Avneet Kaur Harpreet Singh |
| author_sort | Ravneet Kaur |
| collection | DOAJ |
| description | Premature neonates hospitalized in the neonatal intensive care unit (NICU) have high nutritional needs to ensure optimal growth and development. Monitoring adherence to neonatal nutritional guidelines and providing personalized nutritional recommendations are essential for promoting their health. However, ensuring consistent adherence to these guidelines is challenging. To address this, we developed a clinical decision support system using an ontology and rule-based approach to offer personalized nutrition recommendations for preterm infants in the NICU. The Nutrition Recommendation Ontology (NRO) was developed, incorporating 121 classes, 366 axioms, and 157 semantic rules based on standard nutrition guidelines and retrospective data from 601 NICU patients, using a cumulative 8460 patient-days data collected from a single center between 2019-2021. While the ontology represented the conceptual knowledge, the rules encoded the procedural knowledge. The integrated NRO-based system serves as a reasoning engine, enabling the generation of patient-specific feeding recommendations and assisting in identifying deviations from established guidelines. To validate its efficacy, the NRO system was tested on 10 sample case studies and achieved 98% accuracy, as assessed by a panel of neonatologists. Our findings indicate that the NRO-based clinical decision support system can provide accurate personalized nutrition recommendations and assess guideline adherence in the NICU. With further real-world validation, we anticipate that this approach could significantly improve nutrition delivery, prevent malnutrition, and ultimately improve outcomes for preterm infants. |
| first_indexed | 2024-03-08T19:37:13Z |
| format | Article |
| id | doaj.art-4d98df0350d141a595797e87cd732876 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T19:37:13Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4d98df0350d141a595797e87cd7328762023-12-26T00:07:33ZengIEEEIEEE Access2169-35362023-01-011114243314244610.1109/ACCESS.2023.334140310352145An Ontology and Rule-Based Clinical Decision Support System for Personalized Nutrition Recommendations in the Neonatal Intensive Care UnitRavneet Kaur0Monika Jain1Ryan M. McAdams2https://orcid.org/0000-0002-9579-1698Yao Sun3Shubham Gupta4Raghava Mutharaju5https://orcid.org/0000-0003-2421-3935Su Jin Cho6https://orcid.org/0000-0002-3851-9073Satish Saluja7https://orcid.org/0000-0002-3380-123XJonathan P. Palma8Avneet Kaur9Harpreet Singh10https://orcid.org/0000-0001-9104-6060Child Health Imprints (CHIL) Pte. Ltd., Madison, SingaporeDepartment of Computer Science, Indraprastha Institute of Information Technology Delhi, New Delhi, IndiaDepartment of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USADivision of Neonatology, University of California at San Francisco, San Francisco, CA, USAChild Health Imprints (CHIL) Inc., Madison, WI, USADepartment of Computer Science, Indraprastha Institute of Information Technology Delhi, New Delhi, IndiaCollege of Medicine, Ewha Womans University, Seoul, South KoreaDepartment of Neonatology, Sir Ganga Ram Hospital, New Delhi, IndiaOrlando Health Winnie Palmer Hospital for Women & Babies, Orlando, FL, USADepartment of Neonatology, Apollo Cradle Hospital, New Delhi, IndiaChild Health Imprints (CHIL) Inc., Madison, WI, USAPremature neonates hospitalized in the neonatal intensive care unit (NICU) have high nutritional needs to ensure optimal growth and development. Monitoring adherence to neonatal nutritional guidelines and providing personalized nutritional recommendations are essential for promoting their health. However, ensuring consistent adherence to these guidelines is challenging. To address this, we developed a clinical decision support system using an ontology and rule-based approach to offer personalized nutrition recommendations for preterm infants in the NICU. The Nutrition Recommendation Ontology (NRO) was developed, incorporating 121 classes, 366 axioms, and 157 semantic rules based on standard nutrition guidelines and retrospective data from 601 NICU patients, using a cumulative 8460 patient-days data collected from a single center between 2019-2021. While the ontology represented the conceptual knowledge, the rules encoded the procedural knowledge. The integrated NRO-based system serves as a reasoning engine, enabling the generation of patient-specific feeding recommendations and assisting in identifying deviations from established guidelines. To validate its efficacy, the NRO system was tested on 10 sample case studies and achieved 98% accuracy, as assessed by a panel of neonatologists. Our findings indicate that the NRO-based clinical decision support system can provide accurate personalized nutrition recommendations and assess guideline adherence in the NICU. With further real-world validation, we anticipate that this approach could significantly improve nutrition delivery, prevent malnutrition, and ultimately improve outcomes for preterm infants.https://ieeexplore.ieee.org/document/10352145/Premature neonatesontologyclinical decision supportsemanticnutrition management |
| spellingShingle | Ravneet Kaur Monika Jain Ryan M. McAdams Yao Sun Shubham Gupta Raghava Mutharaju Su Jin Cho Satish Saluja Jonathan P. Palma Avneet Kaur Harpreet Singh An Ontology and Rule-Based Clinical Decision Support System for Personalized Nutrition Recommendations in the Neonatal Intensive Care Unit IEEE Access Premature neonates ontology clinical decision support semantic nutrition management |
| title | An Ontology and Rule-Based Clinical Decision Support System for Personalized Nutrition Recommendations in the Neonatal Intensive Care Unit |
| title_full | An Ontology and Rule-Based Clinical Decision Support System for Personalized Nutrition Recommendations in the Neonatal Intensive Care Unit |
| title_fullStr | An Ontology and Rule-Based Clinical Decision Support System for Personalized Nutrition Recommendations in the Neonatal Intensive Care Unit |
| title_full_unstemmed | An Ontology and Rule-Based Clinical Decision Support System for Personalized Nutrition Recommendations in the Neonatal Intensive Care Unit |
| title_short | An Ontology and Rule-Based Clinical Decision Support System for Personalized Nutrition Recommendations in the Neonatal Intensive Care Unit |
| title_sort | ontology and rule based clinical decision support system for personalized nutrition recommendations in the neonatal intensive care unit |
| topic | Premature neonates ontology clinical decision support semantic nutrition management |
| url | https://ieeexplore.ieee.org/document/10352145/ |
| work_keys_str_mv | AT ravneetkaur anontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT monikajain anontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT ryanmmcadams anontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT yaosun anontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT shubhamgupta anontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT raghavamutharaju anontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT sujincho anontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT satishsaluja anontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT jonathanppalma anontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT avneetkaur anontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT harpreetsingh anontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT ravneetkaur ontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT monikajain ontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT ryanmmcadams ontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT yaosun ontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT shubhamgupta ontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT raghavamutharaju ontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT sujincho ontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT satishsaluja ontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT jonathanppalma ontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT avneetkaur ontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit AT harpreetsingh ontologyandrulebasedclinicaldecisionsupportsystemforpersonalizednutritionrecommendationsintheneonatalintensivecareunit |