Lucas Pfeiffer Salomão Dias
ABSTRACT
Context: The main chronic diseases are heart disease, cancers, chronic respiratory diseases, and diabetes, and they are among the leading causes of death worldwide. Problem: Risk factors related to chronic diseases are correlated with people’s lifestyles, and early changes can prevent many chronic disease deaths. Solution: This article proposes an ontology called B-Track Onto that classifies behaviors that attenuate or worsen the risk factors associated with chronic diseases. This ontology works with user behavior profiles and makes recommendations for healthier behaviors. B-Track Onto serves as a knowledge model for information systems that aim to track behaviors associated with risk factors for chronic diseases. SI Theory: The Behavioral Decision Theory was approached, mainly in the incorporation of real patterns of decision making. Method: The ontology has axioms and semantic rules used to provide queries and inferences about its instantiated base. The definition of seven questions allowed inferences to evaluate the ontology. Summary of Results: The MIMIC-III dataset was used as base to import 21 patients from the clinical samples. B-Track Onto inferred all imported patients and categorized them in the expected classes. Besides, this work executed SPARQL queries to answer the competence questions, which returned the expected results for each question. Contributions and Impact in the IS area: B-Track Onto is the first ontology to correlate human behavior and risk factors of chronic diseases, being a potential tool for classifying preventive and non-preventive behaviors. A specialist can use these classification results as part of an Information System in a decision support platform.
- Yousef Alfaifi, Floriana Grasso, and Valentina Tamma. 2018. An Ontology of Psychological Barriers to Support Behaviour Change. In Proceedings of the 2018 International Conference on Digital Health (Lyon, France) (DH ’18). Association for Computing Machinery, New York, NY, USA, 11–15. https://doi.org/10.1145/3194658.3194680Google Scholar
Digital Library
- Muhammad Amith, Jing Wang, Grace Xiong, Kirk Roberts, and Cui Tao. 2020. A health consumer ontology of fast food information. In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 1714–1719. https://doi.org/10.1109/BIBM49941.2020.9313375Google ScholarCross Ref
- Ayan Chatterjee, Andreas Prinz, Martin Gerdes, and Santiago Martinez. 2021. 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 23, 4 (9 Apr 2021), e24656. https://doi.org/10.2196/24656Google ScholarCross Ref
- James C. Griffiths, Jan De Vries, Michael I. McBurney, Suzan Wopereis, Samet Serttas, and Daniel S. Marsman. 2020. Measuring health promotion: translating science into policy. European Journal of Nutrition 59, 2 (01 Sep 2020), 11–23. https://doi.org/10.1007/s00394-020-02359-1Google ScholarCross Ref
- Michael Grüninger and Mark S Fox. 1995. Methodology for the design and evaluation of ontologies. (1995). Available in: < urlhref = "http: //www.eil.utoronto.ca/wp − content/uploads/enterprise − modelling/papers/gruninger − ijcai95.pdf" > http: //www.eil.utoronto.ca/wp − content/uploads/enterprise − modelling/papers/gruninger − ijcai95.pdf < /url >, access: May 22 2022..Google Scholar
- Hatim Guermah, Tarik Fissaa, Bassma Guermah, Hatim Hafiddi, and Mahmoud Nassar. 2018. Using Context Ontology and Linear SVM for Chronic Kidney Disease Prediction. In Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications (Rabat, Morocco) (LOPAL ’18). Association for Computing Machinery, New York, NY, USA, Article 48, 6 pages. https://doi.org/10.1145/3230905.3230941Google ScholarDigital Library
- Hengyi Hu and Larry Kerschberg. 2018. Evolving Medical Ontologies Based on Causal Inference. In Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (Barcelona, Spain) (ASONAM ’18). IEEE Press, 954–957. https://doi.org/10.1109/ASONAM.2018.8508259Google ScholarCross Ref
- Alistair E.W. Johnson, Tom J. Pollard, Lu Shen, Li-wei H. Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G. Mark. 2016. MIMIC-III, a freely accessible critical care database. Scientific Data 3, 1 (24 May 2016), 160035. https://doi.org/10.1038/sdata.2016.35Google ScholarCross Ref
- E.H. Mamdani and S. Assilian. 1975. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7, 1 (1975), 1–13. https://doi.org/10.1016/S0020-7373(75)80002-2Google ScholarCross Ref
- Madison Milne-Ives, Ching Lam, Caroline De Cock, Michelle Helena Van Velthoven, and Edward Meinert. 2020. Mobile Apps for Health Behavior Change in Physical Activity, Diet, Drug and Alcohol Use, and Mental Health: Systematic Review. JMIR Mhealth Uhealth 8, 3 (18 Mar 2020), e17046. https://doi.org/10.2196/17046Google ScholarCross Ref
- Konstantinos Mitsis, Konstantia Zarkogianni, Nefeli Bountouni, Maria Athanasiou, and Konstantina S. Nikita. 2019. An Ontology-Based Serious Game Design for the Development of Nutrition and Food Literacy Skills. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 1405–1408. https://doi.org/10.1109/EMBC.2019.8856604Google ScholarCross Ref
- Mark A. Musen and Protégé Team. 2015. The Protégé Project: A Look Back and a Look Forward. AI matters 1, 4 (Jun 2015), 4–12. https://doi.org/10.1145/2757001.2757003Google ScholarDigital Library
- Palanigurupackiam Nagaraj and Perumalsamy Deepalakshmi. 2022. An intelligent fuzzy inference rule-based expert recommendation system for predictive diabetes diagnosis. International Journal of Imaging Systems and Technology (2022). https://doi.org/10.1002/ima.22710Google ScholarCross Ref
- Sara Nasiri and Mohammad Reza Khosravani. 2020. Progress and challenges in fabrication of wearable sensors for health monitoring. Sensors and Actuators A: Physical 312 (2020), 112105. https://doi.org/10.1016/j.sna.2020.112105Google ScholarCross Ref
- Roberto Pereira and M. Cecilia Baranauskas. 2017. Systemic and Socially Aware Perspective for Information Systems. 148–160. https://doi.org/10.5753/sbc.2884.0.12Google ScholarCross Ref
- Roberto Reda, Filippo Piccinini, and Antonella Carbonaro. 2018. Towards Consistent Data Representation in the IoT Healthcare Landscape. In Proceedings of the 2018 International Conference on Digital Health (Lyon, France) (DH ’18). Association for Computing Machinery, New York, NY, USA, 5–10. https://doi.org/10.1145/3194658.3194668Google ScholarDigital Library
- L. Rehackova, V. Araújo-Soares, S. Steven, A. J. Adamson, R. Taylor, and F. F. Sniehotta. 2020. Behaviour change during dietary Type 2 diabetes remission: a longitudinal qualitative evaluation of an intervention using a very low energy diet. Diabetic Medicine 37, 6 (2020), 953–962. https://doi.org/10.1111/dme.14066Google ScholarCross Ref
- WHO. 2018. Noncommunicable diseases. Available in: < urlhref = "https: //www.who.int/news − room/fact − sheets/detail/noncommunicable − diseases" > https: //www.who.int/news − room/fact − sheets/detail/noncommunicable − diseases < /url >, access: May 22 2022..Google Scholar
- WHO. 2020. WHO reveals leading causes of death and disability worldwide: 2000-2019. Available in: < urlhref = "https: //www.who.int/news/item/09 − 12 − 2020 − who − reveals − leading − causes − of − death − and − disability − worldwide − 2000 − 2019" > https: //www.who.int/news/item/09 − 12 − 2020 − who − reveals − leading − causes − of − death − and − disability − worldwide − 2000 − 2019 < /url >, access: May 22 2022..Google Scholar
- WHO. 2021. Risk factors - Noncommunicable diseases. Available in: < urlhref = "http: //www.emro.who.int/noncommunicable − diseases/causes/index.html" > http: //www.emro.who.int/noncommunicable − diseases/causes/index.html < /url >, access: May 22 2022..Google Scholar
- WHO. 2021. Strengthening NCD service delivery through UHC benefit package: technical meeting report, Geneva, Switzerland, 14-15 July 2020. Available in: < urlhref = "https: //www.who.int/publications/i/item/strengthening − ncd − service − delivery − through − uhc − benefit − package − technical − meeting − report − geneva − switzerland − 14 − 15 − july − 2020" > https: //www.who.int/publications/i/item/strengthening − ncd − service − delivery − through − uhc − benefit − package − technical − meeting − report − geneva − switzerland − 14 − 15 − july − 2020 < /url >, access: May 22 2022..Google Scholar
Index Terms
- Ontology-Based Reasoning to Classify Behaviors Associated with Chronic Disease Risk Factors
Recommendations
An ontology-based approach to patient follow-up assessment for continuous and personalized chronic disease management
Display Omitted We developed customizable assessment models for chronic disease follow-ups.We adapted HL7 Virtual Medical Record for monitoring chronically ill patients.We realized knowledge sharing and reuse in the chronic diseases domain.We developed ...
Research on intelligent extraction of literature knowledge for the risk factors of chronic diseases
Cross-domain Applications of Fuzzy Logic and Machine LearningMedical literature research results are more accurate and representative than patient medical record data. The medical literature intelligently extracts research objects, research areas, and research result entities, identifies their indexing features in ...
An Ontology-Based Electronic Medical Record for Chronic Disease Management
HICSS '11: Proceedings of the 2011 44th Hawaii International Conference on System SciencesEffective chronic disease management ensures better treatment and reduces medical costs. Representing knowledge through building an ontology for Electronic Medical Records (EMRs) is important to achieve semantic interoperability among healthcare ...
Comments