An End-to-End Semantic Platform for Nutritional Diseases Management

The self-management of nutritional diseases requires a system that combines food tracking with the potential risks of food categories on people’s health based on their personal health records (PHRs). The challenges range from the design of an effective food image classification strategy to the development of a full-fledged knowledge-based system. This maps the results of the classification strategy into semantic information that can be exploited for reasoning. However, current works mainly address the single challenges separately without their integration into a whole pipeline. In this paper, we propose a new end-to-end semantic platform where: (i) the classification strategy aims to extract food categories from food pictures; (ii) an ontology is used for detecting the risk factors of food categories for specific diseases; (iii) the Linked Open Data (LOD) Cloud is queried for extracting information concerning related diseases and comorbidities; and, (iv) information from the users’ PHRs are exploited for generating proper personal feedback. Experiments are conducted on a new publicly released dataset. Quantitative and qualitative evaluations, from two living labs, demonstrate the effectiveness and the suitability of the proposed approach

Personalized Nutritional Guidance System to Prevent Malnutrition in Pluripathological Older Patients

Malnutrition is a frequent problem in the elderly population, who usually is affected by one or more pathologies. The health status of these patients can get worsened if malnutrition is left untreated. Nutritional guidelines have been developed to fulfil the nutritional needs derived from certain pathologies, but still are not easy to use. Digital tools can help implement and use these guidelines in real clinical scenarios. Current solutions are designed around a single pathology or specific scenario, but the pluripathologic scenario presents a challenge when it comes to provide nutritional support. In this paper, we present an adaptative tool that provides personalized nutritional recommendations for pluripathological patients in an efficient way, and can be extended to include other pathologies.This study was supported by the grant ZL 2019/00647 NUTRIGEP from Eusko Jaurlaritza (Basque Government) and the European Union under the European Regional Development Fund (ERDF)

Expert System for Nutrition Care Process of Older Adults

This paper presents an expert system for a nutrition care process tailored for the specific needs of elders. Dietary knowledge is defined by nutritionists and encoded as Nutrition Care Process Ontology, and then used as underlining base and standardized model for the nutrition care planning. An inference engine is developed on top of the ontology, providing semantic reasoning infrastructure and mechanisms for evaluating the rules defined for assessing short and long term elders’ self-feeding behaviours, to identify unhealthy dietary patterns and detect the early instauration of malnutrition. Our expert system provides personalized intervention plans covering nutrition education, diet prescription and food ordering adapted to the older adult’s specific nutritional needs, health conditions and food preferences. In-lab evaluation results are presented proving the usefulness and quality of the expert system as well as the computational efficiency, coupling and cohesion of the defined ontology

Design and development of ontology for ai-based software systems to manage the food intake and energy consumption of obesity, diabetes and tube feeding patients

Poor and sedentary lifestyles combined with bad dietary habits have an impact on our health. Nowadays, diet-related diseases have become a major public health issue, threatening the sustainability of healthcare systems, and new strategies to promote better food intake are now being explored. In this context, the use of ontologies has gained importance over the past decade and become more prevalent. By incorporating ontologies in the healthcare domain, artificial intelligence (AI) can be enhanced to better support healthcare systems dealing with chronic diseases, such as obesity and diabetes requiring long-term progress and frequent monitoring. This is especially challenging with current resource inefficiency; however, recent research suggests that incorporating ontology into AI-based technological solutions can improve their accuracy and capabilities. Additionally, recommendation and expert systems benefit from incorporating ontologies for a better knowledge representation and processing to increase success rates. This study outlines the development of an ontology in the context of food intake to manage and monitor patients with obesity, diabetes, and those using tube feeding. A standardized vocabulary for describing food and nutritional information was specified to enable the integration with different healthcare systems and provide personalized dietary recommendations to each user.This research work was developed under the project Food Friend – “Autonomous and easy-to-use tool for monitoring of personal food intake and personalised feedback” (ITEA 18032), co-financed by the North Regional Operational Program (NORTE 2020) under the Portugal 2020 and the European Regional Development Fund (ERDF), with the reference NORTE-01-0247-FEDER-047381 and by National Funds through FCT (Fundação para a Ciência e a Tecnologia) under the project UI/DB/00760/2020. The work of Paulo Novais has been supported by FCT–Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/0031/2020

An Ontology-Based Framework for a Telehealthcare System to Foster Healthy Nutrition and Active Lifestyle in Older Adults

In recent years, telehealthcare systems (TSs) have become more and more widespread, as they can contribute to promoting the continuity of care and managing chronic conditions efficiently. Most TSs and nutrition recommendation systems require much information to return appropriate suggestions. This work proposes an ontology-based TS, namely HeNuALs, aimed at fostering a healthy diet and an active lifestyle in older adults with chronic pathologies. The system is built on the formalization of users' health conditions, which can be obtained by leveraging existing standards. This allows for modeling different pathologies via reusable knowledge, thus limiting the amount of information needed to retrieve nutritional indications from the system. HeNuALs is composed of (1) an ontological layer that stores patients and their data, food and its characteristics, and physical activity-related data, enabling the inference a series of suggestions based on the effects of foods and exercises on specific health conditions; (2) two applications that allow both the patient and the clinicians to access the data (with different permissions) stored in the ontological layer; and (3) a series of wearable sensors that can be used to monitor physical exercise (provided by the patient application) and to ensure patients' safety. HeNuALs inferences have been validated considering two different use cases. The system revealed the ability to determine suggestions for healthy, adequate, or unhealthy dishes for a patient with respiratory disease and for a patient with diabetes mellitus. Future work foresees the extension of the HeNuALs knowledge base by exploiting automatic knowledge retrieval approaches and validation of the whole system with target users

Effects of spermidine supplementation on cognition and biomarkers in older adults with subjective cognitive decline (SmartAge)—study protocol for a randomized controlled trial

Background: Given the global increase in the aging population and age-related diseases, the promotion of healthy aging is one of the most crucial public health issues. This trial aims to contribute to the establishment of effective approaches to promote cognitive and brain health in older individuals with subjective cognitive decline (SCD). Presence of SCD is known to increase the risk of objective cognitive decline and progression to dementia due to Alzheimer’s disease. Therefore, it is our primary goal to determine whether spermidine supplementation has a positive impact on memory performance in this at-risk group, as compared with placebo. The secondary goal is to examine the effects of spermidine intake on other neuropsychological, behavioral, and physiological parameters. Methods: The SmartAge trial is a monocentric, randomized, double-blind, placebo-controlled phase IIb trial. The study will investigate 12 months of intervention with spermidine-based nutritional supplementation (target intervention) compared with 12months of placebo intake (control intervention). We plan to recruit 100 cognitively normal older individuals with SCD from memory clinics, neurologists and general practitioners in private practice, and the general population. Participants will be allocated to one of the two study arms using blockwise randomization stratified by age and sex with a 1:1 allocation ratio. The primary outcome is the change in memory performance between baseline and post-intervention visits (12 months after baseline). Secondary outcomes include the change in memory performance from baseline to follow-up assessment (18months after baseline), as well as changes in neurocognitive, behavioral, and physiological parameters (including blood and neuroimaging biomarkers), assessed at baseline and post-intervention. Discussion: The SmartAge trial aims to provide evidence of the impact of spermidine supplementation on memory performance in older individuals with SCD. In addition, we will identify possible neurophysiological mechanisms of action underlying the anticipated cognitive benefits. Overall, this trial will contribute to the establishment of nutrition intervention in the prevention of Alzheimer’s disease

Machine learning-based recognition on Crowdsourced Food Images

With nearly a third of the world’s population suffering from food-induced chronic diseases such as obesity, the role of food in community health is required now more than ever. While current research underscores food proximity and density, there is a dearth in regard to its nutrition and quality. However, recent research in geospatial data collection and analysis as well as intelligent deep learning will help us study this further. Employing the efficiency and interconnection of computer vision and geospatial technology, we want to study whether healthy food in the community is attainable. Specifically, with the help of deep learning in the field of health geography, we aim to utilize image recognition to gather and model the role of the community food environment in shaping obesity and related chronic diseases

In Silico Approaches and the Role of Ontologies in Aging Research

The 2013 Rostock Symposium on Systems Biology and Bioinformatics in Aging Research was again dedicated to dissecting the aging process using in silico means. A particular focus was on ontologies, as these are a key technology to systematically integrate heterogeneous information about the aging process. Related topics were databases and data integration. Other talks tackled modeling issues and applications, the latter including talks focussed on marker development and cellular stress as well as on diseases, in particular on diseases of kidney and skin