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Spoladore D, Tosi M, Lorenzini EC. Ontology-based decision support systems for diabetes nutrition therapy: A systematic literature review. Artif Intell Med 2024; 151:102859. [PMID: 38564880 DOI: 10.1016/j.artmed.2024.102859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 02/05/2024] [Accepted: 03/25/2024] [Indexed: 04/04/2024]
Abstract
Diabetes is a non-communicable disease that has reached epidemic proportions, affecting 537 million people globally. Artificial Intelligence can support patients or clinicians in diabetes nutrition therapy - the first medical therapy in most cases of Type 1 and Type 2 diabetes. In particular, ontology-based recommender and decision support systems can deliver a computable representation of experts' knowledge, thus delivering patient-tailored nutritional recommendations or supporting clinical personnel in identifying the most suitable diet. This work proposes a systematic literature review of the domain ontologies describing diabetes in such systems, identifying their underlying conceptualizations, the users targeted by the systems, the type(s) of diabetes tackled, and the nutritional recommendations provided. This review also delves into the structure of the domain ontologies, highlighting several aspects that may hinder (or foster) their adoption in recommender and decision support systems for diabetes nutrition therapy. The results of this review process allow to underline how recommendations are formulated and the role of clinical experts in developing domain ontologies, outlining the research trends characterizing this research area. The results also allow for identifying research directions that can foster a preeminent role for clinical experts and clinical guidelines in a cooperative effort to make ontologies more interoperable - thus enabling them to play a significant role in the decision-making processes about diabetes nutrition therapy.
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Affiliation(s)
- Daniele Spoladore
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing - National Research Council, (CNR-STIIMA), Lecco, Italy.
| | - Martina Tosi
- Department of Health Sciences, University of Milan, 20142 Milan, Italy; Institute of Agricultural Biology and Biotechnology - National Research Council (CNR-IBBA), Milan, Italy.
| | - Erna Cecilia Lorenzini
- Department of Biomedical Sciences for Health, University of Milan, I-20133 Milan, Italy.
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Spoladore D, Colombo V, Campanella V, Lunetta C, Mondellini M, Mahroo A, Cerri F, Sacco M. A Knowledge-based Decision Support System for recommending safe recipes to individuals with dysphagia. Comput Biol Med 2024; 171:108193. [PMID: 38387382 DOI: 10.1016/j.compbiomed.2024.108193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 02/13/2024] [Accepted: 02/18/2024] [Indexed: 02/24/2024]
Abstract
BACKGROUND Dysphagia is a disorder that can be associated to several pathological conditions, including neuromuscular diseases, with significant impact on quality of life. Dysphagia often leads to malnutrition, as a consequence of the dietary changes made by patients or their caregivers, who may deliberately decide to reduce or avoid specific food consistencies (because they are not perceived as safe), and the lack of knowledge in how to process foods are critics. Such dietary changes often result in unbalanced nutrients intake, which can have significant consequences for frail patients. This paper presents the development of a prototypical novel ontology-based Decision Support System (DSS) to support neuromuscular patients with dysphagia (following a per-oral nutrition) and their caregivers in preparing nutritionally balanced and safe meals. METHOD After reviewing scientific literature, we developed in collaboration with Ear-Nose-Throat (ENT) specialists, neurologists, and dieticians the DSS formalizes expert knowledge to suggest recipes that are considered safe according to patient's consistency limitations and dysphagia severity and also nutritionally well-balanced. RESULTS The prototype can be accessed via digital applications both by physicians to generate and verify the recommendations, and by the patients and their caregivers to follow the step-by-step procedures to autonomously prepare and process one or more recipe. The system is evaluated with 9 clinicians to assess the quality of the DSS's suggested recipes and its acceptance in clinical practice. CONCLUSIONS Preliminary results suggest a global positive outcome for the recipes inferred by the DSS and a good usability of the system.
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Affiliation(s)
- Daniele Spoladore
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing -National Research Council, (CNR-STIIMA), Lecco, Italy.
| | - Vera Colombo
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing -National Research Council, (CNR-STIIMA), Lecco, Italy
| | - Vania Campanella
- Neuromuscular Omnicentre (NEMO), Fondazione Serena Onlus, Milan, Italy
| | - Christian Lunetta
- Neuromuscular Omnicentre (NEMO), Fondazione Serena Onlus, Milan, Italy; Istituti Clinici Scientifici Maugeri IRCCS, Department of Neurorehabilitation of Milan Institute, Milan, Italy
| | - Marta Mondellini
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing -National Research Council, (CNR-STIIMA), Lecco, Italy
| | - Atieh Mahroo
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing -National Research Council, (CNR-STIIMA), Lecco, Italy
| | - Federica Cerri
- Neuromuscular Omnicentre (NEMO), Fondazione Serena Onlus, Milan, Italy
| | - Marco Sacco
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing -National Research Council, (CNR-STIIMA), Lecco, Italy
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Caon M, Prinelli F, Angelini L, Carrino S, Mugellini E, Orte S, Serrano JCE, Atkinson S, Martin A, Adorni F. PEGASO e-Diary: User Engagement and Dietary Behavior Change of a Mobile Food Record for Adolescents. Front Nutr 2022; 9:727480. [PMID: 35369096 PMCID: PMC8970185 DOI: 10.3389/fnut.2022.727480] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 02/07/2022] [Indexed: 11/13/2022] Open
Abstract
Background Obesity amongst children and adolescents is becoming a major health problem globally and mobile food records can play a crucial role in promoting healthy dietary habits. Objective To describe the methodology for the implementation of the e-Diary mobile food record, to assess its capability in promoting healthy eating habits, to evaluate the factors associated with its usage and engagement. Methods This is a descriptive study that compared the characteristics of participants engaged in the e-Diary, which was part of the PEGASO project in which an app to provide proactive health promotion was given to 365 students at 4 European sites enrolled during October to December 2016: England (UK), Scotland (UK), Lombardy (Italy), and Catalonia (Spain). The e-Diary tracked the users' dietary habits in terms of food groups, dietary indexes, and 6 dietary target behaviors relating to consumption of: fruit; vegetable; breakfast; sugar-sweetened beverages; fast-food; and snacks. The e-Diary provided also personalized suggestions for the next meal and gamification. Results The e-Diary was used for 6 months by 357 adolescents (53.8% females). The study showed that females used the e-Diary much more than males (aOR 3.8, 95% CI 1.6-8.8). Participants aged 14 years were more engaged in the e-Diary than older age groups (aOR 5.1, 95% CI 1.4-18.8) as were those with a very good/excellent self-perceived health status compared to their peers with fair/poor health perception (aOR 4.2, 95% CI 1.3-13.3). Compared to the intervention sites, those living in Catalonia (aOR 13.2 95% CI 2.5-68.8) were more engaged. In terms of behavior change, a significant positive correlation between fruit (p < 0.0001) and vegetables (p = 0.0087) intake was observed in association with increased engagement in the e-Diary. Similarly, adolescents who used the app for more than 2 weeks had significantly higher odds of not skipping breakfast over the study period (aOR 2.5, 95% CI 1.0-6.3). Conclusions The users highly engaged with the e-Diary were associated with improved dietary behaviors: increased consumption of fruit and vegetables and reduced skipping of breakfast. Although the overall usage of the e-Diary was high during the first weeks, it declined thereafter. Future applications should foster user engagement, particularly targeting adolescents at high risk. Clinical Trial Registration https://www.clinicaltrials.gov/, identifier: NCT02930148.
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Affiliation(s)
- Maurizio Caon
- School of Management, University of Applied Sciences and Arts Western Switzerland (HES-SO), Fribourg, Switzerland,*Correspondence: Maurizio Caon
| | - Federica Prinelli
- Institute of Biomedical Technologies, National Research Council, Segrate, Italy
| | - Leonardo Angelini
- School of Management, University of Applied Sciences and Arts Western Switzerland (HES-SO), Fribourg, Switzerland,College of Engineering, University of Applied Sciences and Arts Western Switzerland (HES-SO), Fribourg, Switzerland
| | - Stefano Carrino
- Haute Ecole Arc Ingénierie, University of Applied Sciences and Arts Western Switzerland (HES-SO), St. Imier, Switzerland
| | - Elena Mugellini
- College of Engineering, University of Applied Sciences and Arts Western Switzerland (HES-SO), Fribourg, Switzerland
| | - Silvia Orte
- eHealth Unit, Center Tecnològic de Catalunya (Eurecat), Barcelona, Spain
| | | | - Sarah Atkinson
- Human Factors Research Group, University of Nottingham, Nottingham, United Kingdom
| | - Anne Martin
- United Kingdom Medical Research Council/Chief Scientist Office Social and Public Health Sciences Unit, University of Glasgow, Glasgow, United Kingdom
| | - Fulvio Adorni
- Institute of Biomedical Technologies, National Research Council, Segrate, Italy
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Chatterjee A, Prinz A. OntoRecoModel: Ontological Modeling of Personalized Recommendations for Physical Activity Coaching (Preprint). JMIR Med Inform 2021; 10:e33847. [PMID: 35737439 PMCID: PMC9282669 DOI: 10.2196/33847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 03/05/2022] [Accepted: 04/21/2022] [Indexed: 12/31/2022] Open
Affiliation(s)
- Ayan Chatterjee
- Department of Information and Communication Technology, Center for eHealth, University of Agder, Grimstad, Norway
| | - Andreas Prinz
- Department of Information and Communication Technology, Center for eHealth, University of Agder, Grimstad, Norway
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Chatterjee A, Prinz A, Gerdes M, Martinez S. An Automatic Ontology-Based Approach to Support Logical Representation of Observable and Measurable Data for Healthy Lifestyle Management: Proof-of-Concept Study. J Med Internet Res 2021; 23:e24656. [PMID: 33835031 PMCID: PMC8065560 DOI: 10.2196/24656] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 01/26/2021] [Accepted: 02/08/2021] [Indexed: 12/11/2022] Open
Abstract
Background Lifestyle diseases, because of adverse health behavior, are the foremost cause of death worldwide. An eCoach system may encourage individuals to lead a healthy lifestyle with early health risk prediction, personalized recommendation generation, and goal evaluation. Such an eCoach system needs to collect and transform distributed heterogenous health and wellness data into meaningful information to train an artificially intelligent health risk prediction model. However, it may produce a data compatibility dilemma. Our proposed eHealth ontology can increase interoperability between different heterogeneous networks, provide situation awareness, help in data integration, and discover inferred knowledge. This “proof-of-concept” study will help sensor, questionnaire, and interview data to be more organized for health risk prediction and personalized recommendation generation targeting obesity as a study case. Objective The aim of this study is to develop an OWL-based ontology (UiA eHealth Ontology/UiAeHo) model to annotate personal, physiological, behavioral, and contextual data from heterogeneous sources (sensor, questionnaire, and interview), followed by structuring and standardizing of diverse descriptions to generate meaningful, practical, personalized, and contextual lifestyle recommendations based on the defined rules. Methods We have developed a simulator to collect dummy personal, physiological, behavioral, and contextual data related to artificial participants involved in health monitoring. We have integrated the concepts of “Semantic Sensor Network Ontology” and “Systematized Nomenclature of Medicine—Clinical Terms” to develop our proposed eHealth ontology. The ontology has been created using Protégé (version 5.x). We have used the Java-based “Jena Framework” (version 3.16) for building a semantic web application that includes resource description framework (RDF) application programming interface (API), OWL API, native tuple store (tuple database), and the SPARQL (Simple Protocol and RDF Query Language) query engine. The logical and structural consistency of the proposed ontology has been evaluated with the “HermiT 1.4.3.x” ontology reasoner available in Protégé 5.x. Results The proposed ontology has been implemented for the study case “obesity.” However, it can be extended further to other lifestyle diseases. “UiA eHealth Ontology” has been constructed using logical axioms, declaration axioms, classes, object properties, and data properties. The ontology can be visualized with “Owl Viz,” and the formal representation has been used to infer a participant’s health status using the “HermiT” reasoner. We have also developed a module for ontology verification that behaves like a rule-based decision support system to predict the probability for health risk, based on the evaluation of the results obtained from SPARQL queries. Furthermore, we discussed the potential lifestyle recommendation generation plan against adverse behavioral risks. Conclusions This study has led to the creation of a meaningful, context-specific ontology to model massive, unintuitive, raw, unstructured observations for health and wellness data (eg, sensors, interviews, questionnaires) and to annotate them with semantic metadata to create a compact, intelligible abstraction for health risk predictions for individualized recommendation generation.
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Affiliation(s)
- Ayan Chatterjee
- Department of Information and Communication Technologies, Centre for e-Health, University of Agder, Grimstad, Norway
| | - Andreas Prinz
- Department of Information and Communication Technologies, Centre for e-Health, University of Agder, Grimstad, Norway
| | - Martin Gerdes
- Department of Information and Communication Technologies, Centre for e-Health, University of Agder, Grimstad, Norway
| | - Santiago Martinez
- Department of Health and Nursing Science, Centre for e-Health, University of Agder, Grimstad, Norway
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Caon M, Carrino S, Angelini L, Khaled OA, Mugellini E, Velickovski F, Andreoni G. Teenagers' Usage of a Mobile-Wearable-Cloud Platform to Promote Healthy Lifestyles: the PEGASO Experience .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:1576-1579. [PMID: 30440694 DOI: 10.1109/embc.2018.8512535] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In contemporary society, non-communicable diseases linked to unhealthy lifestyles, such as obesity, are on the rise with a major impact on global deaths. Prevention is the new frontier, promising to increase life expectancy and quality, while reducing costs related to healthcare. The PEGASO project developed a mobile ecosystem where the digital Companion aims at empowering teenagers in the adoption of healthy lifestyles. The pilot study conducted in three European countries (Spain, UK and Italy) shows a good acceptance of the system and that teenagers are keen to use mobile technology to improve their lifestyle, although wearable devices did not engage the young users.
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El-Sappagh S, Kwak D, Ali F, Kwak KS. DMTO: a realistic ontology for standard diabetes mellitus treatment. J Biomed Semantics 2018; 9:8. [PMID: 29409535 PMCID: PMC5800094 DOI: 10.1186/s13326-018-0176-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 01/04/2018] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Treatment of type 2 diabetes mellitus (T2DM) is a complex problem. A clinical decision support system (CDSS) based on massive and distributed electronic health record data can facilitate the automation of this process and enhance its accuracy. The most important component of any CDSS is its knowledge base. This knowledge base can be formulated using ontologies. The formal description logic of ontology supports the inference of hidden knowledge. Building a complete, coherent, consistent, interoperable, and sharable ontology is a challenge. RESULTS This paper introduces the first version of the newly constructed Diabetes Mellitus Treatment Ontology (DMTO) as a basis for shared-semantics, domain-specific, standard, machine-readable, and interoperable knowledge relevant to T2DM treatment. It is a comprehensive ontology and provides the highest coverage and the most complete picture of coded knowledge about T2DM patients' current conditions, previous profiles, and T2DM-related aspects, including complications, symptoms, lab tests, interactions, treatment plan (TP) frameworks, and glucose-related diseases and medications. It adheres to the design principles recommended by the Open Biomedical Ontologies Foundry and is based on ontological realism that follows the principles of the Basic Formal Ontology and the Ontology for General Medical Science. DMTO is implemented under Protégé 5.0 in Web Ontology Language (OWL) 2 format and is publicly available through the National Center for Biomedical Ontology's BioPortal at http://bioportal.bioontology.org/ontologies/DMTO . The current version of DMTO includes more than 10,700 classes, 277 relations, 39,425 annotations, 214 semantic rules, and 62,974 axioms. We provide proof of concept for this approach to modeling TPs. CONCLUSION The ontology is able to collect and analyze most features of T2DM as well as customize chronic TPs with the most appropriate drugs, foods, and physical exercises. DMTO is ready to be used as a knowledge base for semantically intelligent and distributed CDSS systems.
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Affiliation(s)
- Shaker El-Sappagh
- Information Systems Department, Faculty of Computers and Informatics, Benha University, Banha Mansura Road, Meit Ghamr - Benha, Banha, Al Qalyubia Governorate 3000-104 Egypt
| | - Daehan Kwak
- Department of Computer Science, Kean University, Union, NJ 07083 USA
| | - Farman Ali
- Department of Information and Communication Engineering, Inha University, 100 Inharo, Nam-gu, Incheon, 22212 South Korea
| | - Kyung-Sup Kwak
- Department of Information and Communication Engineering, Inha University, 100 Inharo, Nam-gu, Incheon, 22212 South Korea
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