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Accepted for/Published in: JMIR Medical Informatics

Date Submitted: Jul 18, 2023
Date Accepted: Apr 23, 2024

The final, peer-reviewed published version of this preprint can be found here:

An Ontology-Based Decision Support System for Tailored Clinical Nutrition Recommendations for Patients With Chronic Obstructive Pulmonary Disease: Development and Acceptability Study

Spoladore D, Colombo V, Fumagalli A, Tosi M, Lorenzini EC, Sacco M

An Ontology-Based Decision Support System for Tailored Clinical Nutrition Recommendations for Patients With Chronic Obstructive Pulmonary Disease: Development and Acceptability Study

JMIR Med Inform 2024;12:e50980

DOI: 10.2196/50980

PMID: 38922666

PMCID: 11237782

An ontology-based Decision Support System for tailored clinical nutrition recommendations in COPD patients: a preliminary study

  • Daniele Spoladore; 
  • Vera Colombo; 
  • Alessia Fumagalli; 
  • Martina Tosi; 
  • Erna Cecilia Lorenzini; 
  • Marco Sacco

ABSTRACT

Background:

Chronic Obstructive Pulmonary Disease (COPD) is a chronic condition among the main causes of morbidity and mortality worldwide, representing a burden on healthcare systems. Scientific literature highlights that nutrition plays a pivotal role in respiratory inflammatory processes connected to COPD, including exacerbations. Patients with COPD have an increased risk of developing nutrition-related comorbidities, such as diabetes, cardiovascular diseases, and malnutrition. Moreover, these patients often manifest sarcopenia and cachexia. Therefore, an adequate nutritional assessment and therapy are essential to help individuals with COPD in managing the progress of the disease. However, the role of nutrition in Pulmonary Rehabilitation (PR) programs is often underestimated due to a lack of resources and dedicated services, mostly because pneumonologists may lack the specialized training for such a discipline.

Objective:

To support pneumonologists in considering nutritional aspects in PR, this work proposes a novel knowledge-based Decision Support System. The system provides clinicians with patient-tailored dietary recommendations leveraging expert knowledge.

Methods:

The expert knowledge – acquired from both experts and clinical literature – is formalized in domain ontologies and rules, which are developed exploiting the support of Italian clinicians with expertise in the rehabilitation of COPD patients. Thus, by following an agile ontology engineering methodology, the relevant formal ontologies are developed with the aim of acting as a backbone for an application dedicated to pneumonologists. The recommendations provided by the Decision Support System are validated by a group of nutrition experts, while the acceptability of such an application in the context of PR is evaluated by pneumonologists.

Results:

A total of 7 dieticians (age = 46.60 ± 13.35 years) were interviewed to assess their level of agreement with the DSS's recommendations by evaluating 5 patients’ health conditions. The preliminary results indicate that the system performs more than adequately (with an overall average score of 4.23±0.52 out of 5 points), providing meaningful and safe recommendations in compliance with the clinical practice. With regard to the acceptability of the system by lung specialists (age = 44.71 ± 11.94 years), the usefulness and relevance of the proposed solution are extremely positive: TAM3 Perceived Usefulness sub-scales scored 4.86±0.38 each, while the Intention to use scored 4.14±0.38 out of 5 points.

Conclusions:

Although designed for the Italian clinical context, the proposed system can be adapted for any other national clinical context by modifying the domain ontologies, thus providing a multidisciplinary approach to the management of COPD patients. Clinical Trial: Not required.


 Citation

Please cite as:

Spoladore D, Colombo V, Fumagalli A, Tosi M, Lorenzini EC, Sacco M

An Ontology-Based Decision Support System for Tailored Clinical Nutrition Recommendations for Patients With Chronic Obstructive Pulmonary Disease: Development and Acceptability Study

JMIR Med Inform 2024;12:e50980

DOI: 10.2196/50980

PMID: 38922666

PMCID: 11237782

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