A review of domain ontologies for disability representation

Spoladore, Daniele; Sacco, Marco; Trombetta, Alberto

doi:10.1016/j.eswa.2023.120467

Cited by 14 publications

(4 citation statements)

References 72 publications

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“…The EU AI Act is nearing implementation, and it will be the first comprehensive regulation that addresses the risks of artificial intelligence; European Parliament proposed it to ensure better conditions for developing and using this innovative technology ( 172 ). Further, to avoid the risk of hindering AI applications in healthcare due to lack of sufficient transparency “black box,” researchers were urged to provide more research and explanation for AI; explainable AI (xAI), as an approach to more understandable and human-interpretable AI-based applications ( 173 , 174 ).…”

Section: Discussionmentioning

confidence: 99%

The leading global health challenges in the artificial intelligence era

Zaidan

2023

Front. Public Health

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Millions of people’s health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the “digital revolution,” characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.

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Section: Discussionmentioning

confidence: 99%

The leading global health challenges in the artificial intelligence era

Zaidan

2023

Front. Public Health

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“…The inference process simulates human inference capabilities [ 34 ] so that ontology-based approaches are perceived as transparent. Thus, ontologies are widely adopted in several artificial intelligence and health-related applications [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

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

Spoladore,

Colombo,

Fumagalli

et al. 2024

JMIR Med Inform

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Background Chronic obstructive pulmonary disease (COPD) is a chronic condition among the main causes of morbidity and mortality worldwide, representing a burden on health care systems. Scientific literature highlights that nutrition is pivotal 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 pneumologists may lack the specialized training for such a discipline. Objective This work proposes a novel knowledge-based decision support system to support pneumologists in considering nutritional aspects in PR. The system provides clinicians with patient-tailored dietary recommendations leveraging expert knowledge. Methods The expert knowledge—acquired from experts and clinical literature—was formalized in domain ontologies and rules, which were developed leveraging the support of Italian clinicians with expertise in the rehabilitation of patients with COPD. Thus, by following an agile ontology engineering methodology, the relevant formal ontologies were developed to act as a backbone for an application targeted at pneumologists. The recommendations provided by the decision support system were validated by a group of nutrition experts, whereas the acceptability of such an application in the context of PR was evaluated by pneumologists. Results A total of 7 dieticians (mean age 46.60, SD 13.35 years) were interviewed to assess their level of agreement with the decision support system’s recommendations by evaluating 5 patients’ health conditions. The preliminary results indicate that the system performed more than adequately (with an overall average score of 4.23, SD 0.52 out of 5 points), providing meaningful and safe recommendations in compliance with clinical practice. With regard to the acceptability of the system by lung specialists (mean age 44.71, SD 11.94 years), the usefulness and relevance of the proposed solution were extremely positive—the scores on each of the perceived usefulness subscales of the technology acceptance model 3 were 4.86 (SD 0.38) out of 5 points, whereas the score on the intention to use subscale was 4.14 (SD 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 patients with COPD.

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“…Constructing ontologies is used a lot in fields like knowledge graphs, information retrieval, and more. Based on what they cover, ontology model construction can be divided into two groups: general ontology construction and domain ontology construction [19,20]. Previous studies have mostly focused on domain knowledge graph ontology modeling, so this paper proposes a way for constructing a domain ontology.…”

Section: Ontology Modeling Methodologymentioning

confidence: 99%

Construction of an Event Knowledge Graph Based on a Dynamic Resource Scheduling Optimization Algorithm and Semantic Graph Convolutional Neural Networks

Liu,

Zhang,

Zheng

et al. 2023

Electronics

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Presently, road and traffic control construction on most university campuses cannot keep up with the growth of the universities. Campus roads are not very wide, crossings do not have lights, and there are no full-time traffic management personnel. Teachers and students are prone to forming a peak flow of people when going to and from classes. This has led to a constant stream of traffic accidents. It is critical to conduct a comprehensive analysis of this issue by utilizing voluminous data pertaining to school traffic incidents in order to safeguard the lives of faculty and students. In the case of domestic universities, fewer studies have studied knowledge graph construction methods for traffic safety incidents. In event knowledge graph construction, the reasonable release and recycling of computational resources are inefficient, and existing entity–relationship joint extraction methods are unable to deal with ternary overlapping and entity boundary ambiguity problems in relationship extraction. In response to the above problems, this paper proposes a knowledge graph construction method for university on-campus traffic safety events with improved dynamic resource scheduling algorithms and multi-layer semantic graph convolutional neural networks. The experiment’s results show that the proposed dynamic computational resource scheduling method increases GPU and CPU use by 25% and 9%. On the public dataset, the proposed data extraction model’s F1 scores for event triples increase by 1.3% on the NYT dataset and by 0.4% on the WebNLG dataset. This method can help the relevant university personnel in dealing with unexpected traffic incidents and reduce the impact on public opinion.

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A review of domain ontologies for disability representation

Cited by 14 publications

References 72 publications

The leading global health challenges in the artificial intelligence era

The leading global health challenges in the artificial intelligence era

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

Construction of an Event Knowledge Graph Based on a Dynamic Resource Scheduling Optimization Algorithm and Semantic Graph Convolutional Neural Networks

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