Designing an Integrated Platform for Real-Time Recommendations Sharing among the Aged and People Living with Cancer
The world is expected to experience growth in the number of ageing population, and this will bring about high cost of providing care for these valuable citizens. In addition, many of these live with chronic diseases that come with old age. Providing adequate care in the face of rising costs and dwindling personnel can be challenging. However, advances in technologies and emergence of the Internet of Things are providing a way to address these challenges while improving care giving. This study proposes the integration of recommendation systems into homecare to provide real-time recommendations for effective management of people receiving care at home and those living with chronic diseases. Using the simplified Training Logic Concept, stakeholders and requirements were identified. Specific requirements were gathered from people living with cancer. The solution designed has two components namely home and community, to enhance recommendations sharing for effective care giving. The community component of the design was implemented with the development of a mobile app called Recommendations Sharing Community for Aged and Chronically Ill People (ReSCAP). This component has illustrated the possibility of real-time recommendations, improved recommendations sharing among care receivers and between a physician and care receivers. Full implementation will increase access to health data for better care decision making.
[1] Abdul Ghaffar, S.M. Mostafa, A. Alsaleh, T. Sheltami, E.M. Shakshuki, “Internet of Things based multiple disease monitoring and health improvement system”, Journal of Ambient Intelligence and Humanized Computing, pp. 1-9, 2019
[2] H. Ahmadi, G. Arji, L. Shahmoradi, R. Safdari, M. Nilashi, M. Alizadeh,” The application of internet of things in healthcare: a systematic literature review and classification”, Universal Access in the Information Society, pp.1-33, 2018
[3] A. Arens-Volland, B. Gateau, Y. Naudet, Y. (2018, September). Semantic Modeling for Personalized Dietary Recommendation. In 2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), IEEE,pp. 93-98, Sep 2018.
[4] C.H. Chen, M. Karvela, M. Sohbati, T. Shinawatra, C. Toumazou, “PERSON—Personalized Expert Recommendation System for Optimized Nutrition”, IEEE transactions on biomedical circuits and systems, vol. 12 no. 1, pp. 151-160, Feb. 2018
[5] V. Espín, M.V. Hurtado, M. Noguera, “Nutrition for Elder Care: a nutritional semantic recommender system for the elderly”, Expert Systems, vol. 33 no. 2, pp. 201-210, Apr. 2016
[6] J.O. Faronbi, G.O. Faronbi, S.J. Ayamolowo, A.A. Olaogun. "Caring for the Seniors with Chronic Illness: The Lived Experience of Caregivers of Older Adults." Archives of Gerontology and Geriatrics Jan. 2019.
[7] M.K. Hassan, A.I. El Desouky, S.M. Elghamrawy, A.M. Sarhan, “A Hybrid Real-time remote monitoring framework with NB-WOA algorithm for patients with chronic diseases”, Future Generation Computer Systems, vol. 93, pp. 77-95, Apr. 2019
[8] S. Hors-Fraile, O. Rivera-Romero, F. Schneider, L. Fernandez-Luque, F. Luna-Perejon, A. Civit-Balcells, H. de Vries, “Analyzing recommender systems for health promotion using a multidisciplinary taxonomy: A scoping review”, International journal of medical informatics, vol. 114, pp. 143-155, Jun. 2018
[9] W. Hussein, M. R. Ismail, T.F. Gharib, M.G. Mostafa, "A Personalized Recommender System Based on a Hybrid Model." J. UCS, vol. 19 no. 15, pp. 2224-2240, Sep. 2013.
[10] J. L. Katzman, U. Shaham, A. Cloninger, J. Bates, T. Jiang, Y. Kluger, “DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network”, BMC medical research methodology, vol. 18 no. 1, pp.24, Dec. 2018.
[11] P.M. Kumar, U.D.C Gandhi, “A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart diseases”, Computers & Electrical Engineering, vol. 65, pp. 222-235., Jan. 2018.
[12] N. Leipold, M. Madenach, H. Schäfer, M. Lurz, N. Terzimehić, G. Groh, M. Böhm H. Krcmar, “Nutrilize a Personalized Nutrition Recommender System: an enable study”, in HealthRecSys, Vancouver BC, Canada, Oct. 2018.
[13] X. Li, W. Jia, Z. Yang, Y. Li, D. Yuan, H. Zhang, M. Sun, Application of Intelligent Recommendation Techniques for Consumers’ Food Choices in Restaurants. Frontiers in psychiatry, vol. 9, pp.415, 2018.
[14] T.P. Lim, W. Husain, N. Zakaria, “Recommender system for personalised wellness therapy”, International Journal of Advanced Computer Science and Applications, vol. 4, Sep. 2013.
[15] Z. Pang, L. Zheng, J. Tian, S. Kao-Walter, E. Dubrova, Q. Chen, “Design of a terminal solution for integration of in-home health care devices and services towards the Internet-of-Things”, Enterprise Information Systems, vol. 9 no. 1, pp. 86-116, 2015.
[16] S.T.U. Shah, H. Yar, I. Khan, M. Ikram, H. Khan, “Internet of Things-Based Healthcare: Recent Advances and Challenges”, In Applications of Intelligent Technologies in Healthcare, Springer, Cham, pp. 153-162, 2019.
[17] A. Sheth, U. Jaimini, H.Y. Yip, “How Will the Internet of Things Enable Augmented Personalized Health?”, IEEE intelligent systems, vol. 33 no. 1, pp.89-97, Jan. 2018.
[18] V. Subramaniyaswamy, G. Manogaran, R. Logesh, V. Vijayakumar, N. Chilamkurti, D. Malathi, N. Senthilselvan, “An ontology-driven personalized food recommendation in IoT-based healthcare system”, The Journal of Supercomputing, pp.1-33, 2018.
[19] F. Torrent-Fontbona, B. López, “Personalized Adaptive CBR Bolus Recommender System for Type 1 Diabetes”, IEEE journal of biomedical and health informatics, vol. 23 no. 1, pp. 387-394, Jan. 2019.
[20] D. Trihinas, G. Pallis, M. Dikaiakos,” Low-Cost Adaptive Monitoring Techniques for the Internet of Things”, IEEE Transactions on Services Computing, Feb. 2018.
[21] UNFPA (United Nations Population Fund), State of World Population 2018 “The Power of Choice: Reproductive Rights and the Demographic Transition. New York, UNFPA, 2018.
[22] S.L. Wang, Y.L. Chen, A.M.H. Kuo, H.M. Chen, Y.S. Shiu, “Design and evaluation of a cloud-based Mobile Health Information Recommendation system on wireless sensor networks”, Computers & Electrical Engineering, vol. 49, pp. 221-235, Jan. 2016.
[23] F. Wu, X. Li, A.K. Sangaiah, L. Xu, S. Kumari, L. Wu, J. Shen,” A lightweight and robust two-factor authentication scheme for personalized healthcare systems using wireless medical sensor networks”, Future Generation Computer Systems, vol. 82, pp. 727-737, May 2018.
[24] F. Xiao, Q. Miao, X. Xie, L. Sun, R. Wang, “Indoor Anti-Collision Alarm System Based on Wearable Internet of Things for Smart Healthcare”, IEEE Communications Magazine, vol. 56 no. 4, pp. 53-59, Apr. 2018.
[25] Q. Zhang, G. Zhang, J. Lu, D. Wu, “A framework of hybrid recommender system for personalized clinical prescription”, in Intelligent Systems and Knowledge Engineering (ISKE), 2015 10th International Conference on, IEEE, pp. 189-195, Nov. 2015.
[26] H. Hu, A. Elkus, L. Kerschberg, “A Personal Health Recommender System incorporating personal health records, modular ontologies, and crowd-sourced data”, In Advances in Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM International Conference on, IEEE, pp. 1027-1033, Aug. 2016.
[27] L. Qin, X, Xu, J. Li,” A Real-Time Professional Content Recommendation System for Healthcare Providers’ Knowledge Acquisition”, in International Conference on Big Data, Springer, Cham, pp. 367-371, Jun. 2018
[28] A.O. Afolabi, P. Toivanen. “Integration of Recommendation Systems into Connected Health for Effective Management of Chronic Diseases. IEEE Access. Vol. 7, 2019. DOI: 10.1109/ACCESS.2019.2910641
[1] Abdul Ghaffar, S.M. Mostafa, A. Alsaleh, T. Sheltami, E.M. Shakshuki, “Internet of Things based multiple disease monitoring and health improvement system”, Journal of Ambient Intelligence and Humanized Computing, pp. 1-9, 2019
[2] H. Ahmadi, G. Arji, L. Shahmoradi, R. Safdari, M. Nilashi, M. Alizadeh,” The application of internet of things in healthcare: a systematic literature review and classification”, Universal Access in the Information Society, pp.1-33, 2018
[3] A. Arens-Volland, B. Gateau, Y. Naudet, Y. (2018, September). Semantic Modeling for Personalized Dietary Recommendation. In 2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), IEEE,pp. 93-98, Sep 2018.
[4] C.H. Chen, M. Karvela, M. Sohbati, T. Shinawatra, C. Toumazou, “PERSON—Personalized Expert Recommendation System for Optimized Nutrition”, IEEE transactions on biomedical circuits and systems, vol. 12 no. 1, pp. 151-160, Feb. 2018
[5] V. Espín, M.V. Hurtado, M. Noguera, “Nutrition for Elder Care: a nutritional semantic recommender system for the elderly”, Expert Systems, vol. 33 no. 2, pp. 201-210, Apr. 2016
[6] J.O. Faronbi, G.O. Faronbi, S.J. Ayamolowo, A.A. Olaogun. "Caring for the Seniors with Chronic Illness: The Lived Experience of Caregivers of Older Adults." Archives of Gerontology and Geriatrics Jan. 2019.
[7] M.K. Hassan, A.I. El Desouky, S.M. Elghamrawy, A.M. Sarhan, “A Hybrid Real-time remote monitoring framework with NB-WOA algorithm for patients with chronic diseases”, Future Generation Computer Systems, vol. 93, pp. 77-95, Apr. 2019
[8] S. Hors-Fraile, O. Rivera-Romero, F. Schneider, L. Fernandez-Luque, F. Luna-Perejon, A. Civit-Balcells, H. de Vries, “Analyzing recommender systems for health promotion using a multidisciplinary taxonomy: A scoping review”, International journal of medical informatics, vol. 114, pp. 143-155, Jun. 2018
[9] W. Hussein, M. R. Ismail, T.F. Gharib, M.G. Mostafa, "A Personalized Recommender System Based on a Hybrid Model." J. UCS, vol. 19 no. 15, pp. 2224-2240, Sep. 2013.
[10] J. L. Katzman, U. Shaham, A. Cloninger, J. Bates, T. Jiang, Y. Kluger, “DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network”, BMC medical research methodology, vol. 18 no. 1, pp.24, Dec. 2018.
[11] P.M. Kumar, U.D.C Gandhi, “A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart diseases”, Computers & Electrical Engineering, vol. 65, pp. 222-235., Jan. 2018.
[12] N. Leipold, M. Madenach, H. Schäfer, M. Lurz, N. Terzimehić, G. Groh, M. Böhm H. Krcmar, “Nutrilize a Personalized Nutrition Recommender System: an enable study”, in HealthRecSys, Vancouver BC, Canada, Oct. 2018.
[13] X. Li, W. Jia, Z. Yang, Y. Li, D. Yuan, H. Zhang, M. Sun, Application of Intelligent Recommendation Techniques for Consumers’ Food Choices in Restaurants. Frontiers in psychiatry, vol. 9, pp.415, 2018.
[14] T.P. Lim, W. Husain, N. Zakaria, “Recommender system for personalised wellness therapy”, International Journal of Advanced Computer Science and Applications, vol. 4, Sep. 2013.
[15] Z. Pang, L. Zheng, J. Tian, S. Kao-Walter, E. Dubrova, Q. Chen, “Design of a terminal solution for integration of in-home health care devices and services towards the Internet-of-Things”, Enterprise Information Systems, vol. 9 no. 1, pp. 86-116, 2015.
[16] S.T.U. Shah, H. Yar, I. Khan, M. Ikram, H. Khan, “Internet of Things-Based Healthcare: Recent Advances and Challenges”, In Applications of Intelligent Technologies in Healthcare, Springer, Cham, pp. 153-162, 2019.
[17] A. Sheth, U. Jaimini, H.Y. Yip, “How Will the Internet of Things Enable Augmented Personalized Health?”, IEEE intelligent systems, vol. 33 no. 1, pp.89-97, Jan. 2018.
[18] V. Subramaniyaswamy, G. Manogaran, R. Logesh, V. Vijayakumar, N. Chilamkurti, D. Malathi, N. Senthilselvan, “An ontology-driven personalized food recommendation in IoT-based healthcare system”, The Journal of Supercomputing, pp.1-33, 2018.
[19] F. Torrent-Fontbona, B. López, “Personalized Adaptive CBR Bolus Recommender System for Type 1 Diabetes”, IEEE journal of biomedical and health informatics, vol. 23 no. 1, pp. 387-394, Jan. 2019.
[20] D. Trihinas, G. Pallis, M. Dikaiakos,” Low-Cost Adaptive Monitoring Techniques for the Internet of Things”, IEEE Transactions on Services Computing, Feb. 2018.
[21] UNFPA (United Nations Population Fund), State of World Population 2018 “The Power of Choice: Reproductive Rights and the Demographic Transition. New York, UNFPA, 2018.
[22] S.L. Wang, Y.L. Chen, A.M.H. Kuo, H.M. Chen, Y.S. Shiu, “Design and evaluation of a cloud-based Mobile Health Information Recommendation system on wireless sensor networks”, Computers & Electrical Engineering, vol. 49, pp. 221-235, Jan. 2016.
[23] F. Wu, X. Li, A.K. Sangaiah, L. Xu, S. Kumari, L. Wu, J. Shen,” A lightweight and robust two-factor authentication scheme for personalized healthcare systems using wireless medical sensor networks”, Future Generation Computer Systems, vol. 82, pp. 727-737, May 2018.
[24] F. Xiao, Q. Miao, X. Xie, L. Sun, R. Wang, “Indoor Anti-Collision Alarm System Based on Wearable Internet of Things for Smart Healthcare”, IEEE Communications Magazine, vol. 56 no. 4, pp. 53-59, Apr. 2018.
[25] Q. Zhang, G. Zhang, J. Lu, D. Wu, “A framework of hybrid recommender system for personalized clinical prescription”, in Intelligent Systems and Knowledge Engineering (ISKE), 2015 10th International Conference on, IEEE, pp. 189-195, Nov. 2015.
[26] H. Hu, A. Elkus, L. Kerschberg, “A Personal Health Recommender System incorporating personal health records, modular ontologies, and crowd-sourced data”, In Advances in Social Networks Analysis and Mining (ASONAM), 2016 IEEE/ACM International Conference on, IEEE, pp. 1027-1033, Aug. 2016.
[27] L. Qin, X, Xu, J. Li,” A Real-Time Professional Content Recommendation System for Healthcare Providers’ Knowledge Acquisition”, in International Conference on Big Data, Springer, Cham, pp. 367-371, Jun. 2018
[28] A.O. Afolabi, P. Toivanen. “Integration of Recommendation Systems into Connected Health for Effective Management of Chronic Diseases. IEEE Access. Vol. 7, 2019. DOI: 10.1109/ACCESS.2019.2910641
@article{"International Journal of Information, Control and Computer Sciences:78690", author = "Adekunle O. Afolabi and Pekka Toivanen", title = "Designing an Integrated Platform for Real-Time Recommendations Sharing among the Aged and People Living with Cancer", abstract = "The world is expected to experience growth in the number of ageing population, and this will bring about high cost of providing care for these valuable citizens. In addition, many of these live with chronic diseases that come with old age. Providing adequate care in the face of rising costs and dwindling personnel can be challenging. However, advances in technologies and emergence of the Internet of Things are providing a way to address these challenges while improving care giving. This study proposes the integration of recommendation systems into homecare to provide real-time recommendations for effective management of people receiving care at home and those living with chronic diseases. Using the simplified Training Logic Concept, stakeholders and requirements were identified. Specific requirements were gathered from people living with cancer. The solution designed has two components namely home and community, to enhance recommendations sharing for effective care giving. The community component of the design was implemented with the development of a mobile app called Recommendations Sharing Community for Aged and Chronically Ill People (ReSCAP). This component has illustrated the possibility of real-time recommendations, improved recommendations sharing among care receivers and between a physician and care receivers. Full implementation will increase access to health data for better care decision making.
", keywords = "Recommendation systems, healthcare, internet of things, real-time, homecare.", volume = "13", number = "4", pages = "235-5", }
