The use of machine learning approach in the field of health informatics improves the quality and effectiveness of decision-making process. Its particular integration into the Remote Health Monitoring Systems (RHMS) allows: collecting, analyzing and learning from real-time data, automatically gaining knowledge and making predictions on the patients state. The RHMS represent an effective solution to control and monitor a growing number of dependent or elderly patients. They showed impressive results in healthcare applications. These systems are built around the sensors installed on the body of the patient and others embedded in their environments. In this context, we are confronted with the challenge of temporal aspect of data. To meet this challenge, we propose that RHMS combine machine learning for generating intelligent valuable information, and visual analytics for gaining insight the collected real-time data. The success of these applications is based on the quality of their design and development. For this reason, we propose to design a RHMS using multi-agent technology. The developed prototype was evaluated to verify our proposal feasibility.
FoxJ.BeveridgeM. and GlasspoolD., Understanding intelligent agents: analysis and synthesis, AI Communications16(3) (2003), 139–152.
2.
KhannaA. and MisraP., White Paper Life Sciences, The Internet of Things for Medical Devices-Prospects, Challenges and the Way Forward, Tata Consultancy Services, July 1, 2014.
3.
AnnicchiaricoR.CortésU. and UrdialesC., eds), Agent Technology and e-Health, Whitestein Series in Software Agent Technologies and Autonomic Computing, BirkhäuserVerlag, Basel, Switzerland, 2008.
4.
NealonJ.L. and MorenoA., eds, Applications of Software Agent Technology in the Health Care Domain, Whitestein Series in Software Agent Technologies and Autonomic Computing, BirkhäuserVerlag, Basel, Switzerland, 2003.
5.
AnielloM.MassimoE. and GiuseppeD.P., Design and validation of a light-weight reasoning system to support remote health monitoring application, Engineering Applications of Artificial Intelligence41 (2015), pp. 232–248, ISSN 0952-1976.
6.
ChengL.ZhuM.PossJohnJ.W.HirdesP.GlennyC. and StoleeP., Opinion versus practice regarding the use of rehabilitation services in home care: an investigation using machine learning algorithms, BMC Medical Informatics and Decision Making, Department of Statistics and Actuarial ScienceUniversity of Waterloo, Waterloo, Canada, 2015.
7.
Revuelta-ZamoranoP.SánchezA.Luis Rojo-ÁlvarezJ.Álvarez-RodríguezJ.Ramos-LópezJ. and Soguero-RuizC., Prediction of Healthcare Associated Infections in an Intensive Care Unit Using Machine Learning and Big Data Tools, Springer International Publishing Switzerland 2016KyriacouE. et al., XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016, IFMBE Proceedings 57.
8.
Sathiya RaniN.VimalaK. and KalaivaniV., A Remote Healthcare Monitoring System for Faster Identification of Cardiac Abnormalities from Compressed ECG Using Advanced Data Mining Approach, In: KumarS.M. eds, Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012). Lecture Notes in Electrical Engineering, vol 222. Springer, India.
9.
HolzingerA., Human-Computer Interaction and Knowledge Discovery (HCI-KDD): What Is the Benefit of Bringing Those Two Fields to Work Together? International Conference on Availability, Reliability, and Security 2013, pp. 319–328.
10.
AtzoriL.IeraA. and MorabitoG., The Internet of things: A survey, Computer Networks54(15) (2010), 2787–2805.
11.
PietteF. and CavalC., A Multi-Agent System for Resource Privacy: Deployment of Ambient Applications in Smart Environments, Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2016).
12.
MuhammadS.R.AslamM. and MartnezEnriquezA.M., Remote Patient Monitoring and Healthcare Management using Multi-Agent based Architecture, Ninth Mexican International Conference on Artificial Intelligence, 978-0-7695-4284-3/10 2010 IEEE.
13.
ChanV.RayP. and ParameswaranN., A Multi-Agent Collaborative Framework for Mobile E-HealthSystem Sciences, 2007. HICSS 2007, 40th Annual Hawaii Int. Conf.
14.
LanM.AlshurafaN.GhasemzadehH. and SarrafzadehM., WANDA: An End-to-End Remote Health Monitoring and Analytics System for Heart Failure Patients, Wireless Health Conf., 2012.
15.
LeeS.I.GhasemzadehH.MortazaviB.J.LanM.AlshurafaN.OngM. and SarrafzadehM., Remote Health Monitoring Systems: What Impact Can Data Analytics Have on Cost? in Wireless Health, 2013.
16.
TengX.F.ZhangY.T.PoonC.Y. and BonatoP., Wearable Medical Systems for P-Health, IEEE Rev Biomed Eng1 (2008), 62–74.
17.
AgoulmineN.RayP. and WuT.H., Communications in Ubiquitous Healthcare, IEEECommun Mag50 (2012), 16–18.
18.
MladenicD.LavracN.BohanecM. and MoyleS., Data mining and Decision Support: integration and collaboration, Springer-Verlag New York Inc., (2012), p. 304.
19.
IamsumangC.MoslehA. and ModarresM., Hybrid DBN monitoring and anomaly detection algorithms for on-line SHM, 2015 Annual Reliability and Maintainability Symposium (RAMS), Palm Harbor, FL, IEEE, 2015, pp. 1–7.
20.
LtifiH.KolskiC. and Ben AyedM., Combination of cognitive and HCI modeling for the design of KDD-based DSS used in dynamicsituations, Decision SupportSystems78 (2015), Elsevier, 51–64.
21.
RandevK., Data Mining for Structural Health Monitoring, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited, United Technologies Research Center, University of Minnesota USA, 2009, pp. 450–457.
22.
YooI.AlafaireetP.MarinovM.Pena-HernandezK.GopidiR.ChangJ.F. and HuaL., Data mining in healthcare and biomedicine: A survey of the literature, J Med Syst36 (2012), 2431–2448.
23.
SunF.T.KuoC.ChengH.T.ButhpitiyaS.CollinsP. and GrissM., Activity-Aware Mental Stress Detection Using Physiological Sensors, In Mobile Computing, Applications, and Services; GrisM. and YangG., Eds, Springer: Berlin, Germany, Volume 76, 2012, pp. 211–230.
24.
NangaliaV.PrytherchD. and SmithG., Health technology assessment review: Remote monitoring of vital signs – current status and future challenges, Crit Care14 (2010), 1–8.
25.
LakshmananV.GillelandE.McGovernA. and TingleyM., Machine Learning and Data Mining Approaches to Climate Science, Proceedings of the 4th International Workshop on Climate Informatics, ISBN: 978-3-319-17219-4, 2015.
RiccioG.CavuotiS.SchisanoE.BresciaM.MercurioA.EliaD.BenedettiniM.PezzutoS.MolinariS. and Maria Di GiorgioA., Machine Learning Based Data Mining for Milky Way Filamentary Structures Reconstruction, In: BassisS.EspositoA.MorabitoF. and PaseroE., eds, Advances in Neural Networks. Smart Innovation, Systems and Technologies, vol 54. Springer, Cham, 2016.
28.
WuC.WengY.JiangQ.GuoW. and WangC., Applied research on visual mining technology in medical data, 2016 4th International Conference on Cloud Computing and Intelligence Systems (CCIS).
29.
ElouniJ.LtifiH. and BenAyedM., Knowledge visualization model for intelligent dynamic decision-making, The 15th International Conference on Hybrid Intelligent Systems (HIS 2015) in Seoul, South Korea, November 2015, pp. 223–235.
30.
ElouniJ.LtifiH.Ben AyedM. a nd MasmoudiM., Visual knowledge generation from data mining patterns for decision-making, International Journal of Advanced Computer Science and Applications (IJACSA). – Volume 7 Issue 7 July 2016.
31.
LtifiH.Ben MohamedE. and Ben AyedM., Interactive visual knowledge discovery from data-based temporal decision support system, Information Visualization15(1) (2016), 31–50.
32.
FosterD.McGregorC. and El-MasriS., A survey of agent-based intelligent decision support systems to support clinical management and research, in: ArmanoG.MerelliE.DenzingerJ.MartinA.MilesS.TianfieldH. and UnlandR. Eds, Proceedings of MAS-BIOMED’05, Utretch, Netherlands, 2005.
33.
NealonJ.L. and MorenoA., Eds, Applications of Software Agent Technology in the Health Care Domain, Whitestein Series in Software Agent Technologies and Autonomic Computing, BirkhäuserVerlag, Basel, Switzerland, 2003.
34.
AnnicchiaricoR.CortésU. and UrdialesC., eds, Agent Technology and e-Health, Whitestein Series in Software Agent Technologies and Autonomic Computing, BirkhäuserVerlag, Basel, Switzerland, 2008.
35.
BenjemmaaA.LtifiH. and Ben AyedM., Multi-Agent architecture for Visual Intelligent Remote Healthcare Monitoring System, The 15th International Conference on Hybrid Intelligent Systems (HIS 2015) in Seoul, South Korea, November 2015, pp. 223–235.
36.
MusiatP. and HoffmannL., Personalised computerised feedback in E-mental health, Journal of Mental Health21(4) (2012), 346–354.
37.
FilipeL.Fdez-RiverolaF.CostaN. and PereiraA., Wireless Body Area Networks for Healthcare Applications: Protocol Stack Review, International Journal of Distributed Sensor Networks2015 (2015), 1–23.
38.
KhanP.HussainM.A. and KwakK.S., Medical applications of wireless body area networks, International Journal of Digital Content Technology and its Applications3(3) (2009).
39.
MoosaviS.GiaT.NigussieE.RahmaniA.VirtanenaS.TenhunenaH. and IsoahoaJ., End-to-end security scheme for mobility enabled healthcare Internet of Things, Future Generation Computer Systems64 (2016), 108–124.
40.
EllouziH.LtifiH. and Ben AyedM., New Multi-agent Architecture of Visual Intelligent Decision Support Systems Application in the medical field, 13th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA, Marrakch, Marroc, Novembre 2015.
41.
EllouziH.LtifiH. and Ben AyedM., Modeling of distributed visual Knowledge discovery from data process, International Conference on intelligent systems and knowledge engeneering, ISKE, Nanjing, China, Novembre 2017.
42.
LtifiH.Ben AyedM.KolskiC. and AlimiA., HCI-enriched approach for DSS development: the UP/U approach, IEEE Symposium on Computers and Communications (ISCC’09, July 5–8), IEEE Press, Sousse, Tunisia, 2009, pp. 895–900.
43.
EllouziH.LtifiH. and Ben AyedM., Multi-agents modelling of Decision Support Systems based on visual data mining, Multi Agent and Grid Systems, Multi Agent and Grid Systems – An International Journal13(1) (2017), 31–45.
44.
LeiteA. and GirardiR., A hybrid and learning agent architecture for network intrusion detection, Journal of Systems and Software2017, pp. 59–80.
45.
SoumyajitG.ShreeasishK. and MonideepaR., Dynamic Service Deployment Using JSON Based Decision Trees in Remote Healthcare, Intelligent Systems and Control (ISCO), 2016 10 International Conference, School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India.
46.
El HaddiouiA.El MamouneS. and LoubnaC., New system forecast e-health by using hybrid of association rules and decision tree, 2014 5th Workshop on Codes, Cryptography and Communication Systems (WCCCS) university, Faculty of Sciences, Tetuan, Morocco.
47.
ManogaranG.VaratharajanR.LopezD.KumarP.SundarasekarR. and ThotaC., A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting, Future Generation Computer Systems82 (2018), 375–387.
48.
DotoliM.EpicocoN.FalagarioM.SeatzuC. and TurchianoB., A decision support system for optimizing operations at intermodal railroad terminals, IEEE Transactions on Systems, Man, and Cybernetics: Systems47(3), 2017.
