ABSTRACT
In the first generation of interventions and home support, the needs were triggered generally by phone call and they are described orally. In most cases, there is no registered information about the patient's conditions or medical history. The interveners are either from the private or state health sector. Despite the fact that there are people who can intervene quicker than others, yet they have not been recruited, or work as professional liberal. With the development of technology, systems based on data mining or artificial intelligence have been developed to focus on the intervention's time for example. Although intervention and home-based care are the subject of many studies [1], the resolution of the overall decision-making problem is not sufficiently developed. On the one hand, the state of health presupposes the definition of a patient. There is a set of parameters characterizing the habits of daily life of the person analyzed in parallel with the evolution of physiological and environment. On the other hand, it is necessary to take into consideration the medical Core, his location, his profile not only his professional status but also his abilities and skills that are not explicitly described in his curriculum. Different studies and systems exist in the literature [2]. Each of his studies tackles only a part of the parameters. Indeed, these studies consider either the monitoring of daily activities, the monitoring of physiological data or other environmental parts. Either they consider the specificities of the medical Core's profile, or these systems use a probabilistic data mining that involves many interactions with the experts to interpret the data, either wise an expert system based on the inference rules defined by the medical experts. In addition, most systems do not use controlled vocabulary that provides semantics needed. This complicates information sharing and collaborative work. The objective of the e-SAAD project is to propose a methodological process to facilitate the analysis and procedure of intervention systems and home support. The process should identify the generic and specific aspects of each part. The patient's data set, profile, history, its environment and location should be taken into consideration. As well as the service providers, their profiles, their skills and essentially their availability and locations. These models must be open to be adapted to new data sources.
- Yassine, A., Singh, S., & Alamri, A. (2017). Mining Human Activity Patterns From Smart Home Big Data for Health Care Applications. IEEE Access, 5, 13131--13141. doi:10.1109/access.2017.2719921Google ScholarCross Ref
- M. S. Hossain, "Cloud-supported cyber-physical localizationframework for patients monitoring," IEEE Systems Journal, vol. 11, no. 1, pp. 118--127, March 2017.Google Scholar
- Hajem, S., Saidi, O., Ben Mansour, N., Mejdoub, Y., & Hsairi, M. (2014). Epidemiology of dementia in Tunisia. NPG Neurology -- PsychiatryGoogle Scholar
- Valls, A., Gibert, K., Sánchez, D., & Batet, M. (2010). Using ontologies for structuring organizational knowledge in Home Care assistance. International Journal of Medical Informatics, 79(5), 370--387. doi:10.1016/j.ijmedinf.2010.01.012Google ScholarCross Ref
- Tang, V., Siu, P. K. Y., Choy, K. L., Lam, H. Y., Ho, G. T. S., Lee, C. K. M., & Tsang, Y. P. (2019). An adaptive clinical decision support system for serving the elderly with chronic diseases in healthcare industry. Expert Systems, e12369.Google Scholar
- Shoaip, N., El-Sappagh, S., Barakat, S., & Elmogy, M. (2019). Ontology enhanced fuzzy clinical decision support system. U-Healthcare Monitoring Systems, 147--177. doi:10.1016/b978-0-12-815370-3.00007-4.Google Scholar
- Benfares, C., Idrissi, Y. E. B. E., & Hamid, K. (2019). Personalized Healthcare System Based on Ontologies. Coastal Research Library, 185--196. doi:10.1007/978-3-030-11884-6_18Google ScholarCross Ref
- Buyya, R., Calheiros, R. N., & Dastjerdi, A. V. (Eds.). (2016). Big data: principles and paradigms. Morgan Kaufmann.Google Scholar
- Rajbhandari, S., Aryal, J., Osborn, J., Lucieer, A., & Musk, R. (2019). Leveraging Machine Learning to Extend Ontology-Driven Geographic Object-Based Image Analysis (O-GEOBIA): A Case Study in Forest-Type Mapping. Remote Sensing, 11(5), 503. doi:10.3390/rs11050503Google ScholarCross Ref
- International Journal of Approximate ReasoningVolume 91, December 2017, Pages 56--79Google Scholar
- Nouveau règlement européen des dispositifs médicaux: comment l'écosystème français doit saisir l'opportunité d'EUDAMED et du système IUD, tout en dépassant les contraintes Therapies, Volume 74, Issue 1, February 2019, Pages 59--72Google Scholar
- Bernasconi, A., & Masseroli, M. (2018). Biological and Medical Ontologies: Disease Ontology (DO). Reference Module in Life Sciences. doi:10.1016/b978-0-12-809633-8.20397-xGoogle Scholar
- Hobbs, J. R., & Pan, F. (2004). An ontology of time for the semantic web. ACM Transactions on Asian Language Information Processing, 3(1), 66--85.Google ScholarDigital Library
- Acheson, E., De Sabbata, S., & Purves, R. S. (2017). A quantitative analysis of global gazetteers: Patterns of coverage for common feature types. Computers, Environment and Urban Systems, 64, 309--320.Google ScholarCross Ref
- Formalizing Enrichment Mechanisms for Bibliographic Ontologies in the Semantic Web: 12th International Conference, MTSR 2018, Limassol, Cyprus, October 23-26, 2018Google Scholar
- Zulfiqar, A. A., Hajjam, A., Gény, B., Talha, S., Hajjam, M., Hajjam, J., Andrès, E. (2019). Telemedicine and Cardiology in the Elderly in France: Inventory of Experiments. Advances in Preventive Medicine, 2019, 1--7. doi:10.1155/2019/2102156.Google ScholarCross Ref
- Monsen, K. A., Maganti, S., Giaquinto, R. A., Mathiason, M. A., Bjarnadottir, R. I., & Kreitzer, M. J. (2018). Use of the Omaha System for ontology-based text mining to discover meaning within CaringBridge social media journals. Kontakt. doi:10.1016/j.kontakt.2018.03.002Google Scholar
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