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Usage of Mobile Technologies for Diseases Inference: A Literature Review

Published: 09 June 2021 Publication History

Abstract

The fields of artificial intelligence, knowledge inference, data science, etc. have been deeply studied over time and many theoretical approaches have been developed, including its application to health and diseases inference. The creation of prototype and consumer systems has been restrained by the technology limitations on data acquisition and processing, which has been greatly overcome with the new sensors and mobile devices technologies. So, in this work we go through a literature review of the current state of the art on record to the usage of mobile technologies for diseases inference. The review methodology is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The criteria were based on journal articles, prior to 2008, and using the defined keywords. A total of 14 selected articles were analyzed. A general conclusion was attained regarding the current state of maturity of the field, leading to fully functional consumer and professional market products.

References

[1]
Alepis, E. and Lambrinidis, C., 2013. M-health: supporting automated diagnosis and electonic health records. SpringerPlus 2, 1, 103.
[2]
Benlamri, R. and Docksteader, L., 2010. MORF: A mobile health-monitoring platform. IT professional 12, 3, 18-25.
[3]
Chan, V., Ray, P., and Parameswaran, N., 2008. Mobile e-Health monitoring: an agent-based approach. IET communications 2, 2, 223-230.
[4]
Chang, S.-H., Chiang, R.-D., Wu, S.-J., and Chang, W.-T., 2016. A context-aware, interactive M-health system for diabetics. IT professional 18, 3, 14-22.
[5]
Chowdhury, A.R., Falchuk, B., and Misra, A., 2010. Medially: A provenance-aware remote health monitoring middleware. In 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom) IEEE, 125-134.
[6]
Copetti, A., Leite, J.C., Loques, O., and Neves, M.F., 2013. A decision-making mechanism for context inference in pervasive healthcare environments. Decision Support Systems 55, 2, 528-537.
[7]
Elhelw, M., Pansiot, J., Mcilwraith, D., Ali, R., Lo, B., and Atallah, L., 2009. An integrated multi-sensing framework for pervasive healthcare monitoring. In 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare IEEE, 1-7.
[8]
Esposito, A., Tarricone, L., Zappatore, M., Catarinucci, L., and Colella, R., 2010. A framework for context-aware home-health monitoring. International Journal of Autonomous and Adaptive Communications Systems 3, 1, 75-91.
[9]
Fayn, J. and Rubel, P., 2009. Toward a personal health society in cardiology. IEEE Transactions on Information technology in Biomedicine 14, 2, 401-409.
[10]
Forkan, A.R.M., Khalil, I., Ibaida, A., and Tari, Z., 2015. BDCaM: Big data for context-aware monitoring—A personalized knowledge discovery framework for assisted healthcare. IEEE transactions on cloud computing 5, 4, 628-641.
[11]
Guo, J., Zhou, X., Sun, Y., Ping, G., Zhao, G., and Li, Z., 2016. Smartphone-based patients’ activity recognition by using a self-learning scheme for medical monitoring. Journal of medical systems 40, 6, 140.
[12]
Huang, A., Chen, C., Bian, K., Duan, X., Chen, M., Gao, H., Meng, C., Zheng, Q., Zhang, Y., and Jiao, B., 2013. WE-CARE: an intelligent mobile telecardiology system to enable mHealth applications. IEEE journal of biomedical and health informatics 18, 2, 693-702.
[13]
Huang, A., Xu, W., Li, Z., Xie, L., Sarrafzadeh, M., Li, X., and Cong, J., 2013. System light-loading technology for mHealth: manifold-learning-based medical data cleansing and clinical trials in WE-CARE project. IEEE journal of biomedical and health informatics 18, 5, 1581-1589.
[14]
International Telecommunication Union, 2019. Measuring digital development: Facts and figures 2019 ITU Publications.
[15]
Kao, H.-C., Tang, K.-F., and Chang, E.Y., 2018. Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning. In AAAI, 2305-2313.
[16]
Kumar, P.M., Lokesh, S., Varatharajan, R., Babu, G.C., and Parthasarathy, P., 2018. Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier. Future Generation Computer Systems 86, 527-534.
[17]
Maity, N.G. and Das, S., 2017. Machine learning for improved diagnosis and prognosis in healthcare. In 2017 IEEE Aerospace Conference IEEE, 1-9.
[18]
Massey, T., Marfia, G., Potkonjak, M., and Sarrafzadeh, M., 2009. Experimental analysis of a mobile health system for mood disorders. IEEE Transactions on Information technology in Biomedicine 14, 2, 241-247.
[19]
Mohammed, A. and Demosthenous, A., 2018. Complementary detection for hardware efficient on-site monitoring of Parkinsonian progress. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 8, 3, 603-615.
[20]
Moher, D., Liberati, A., Tetzlaff, J., and Altman, D.G., 2010. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Int J Surg 8, 5, 336-341.
[21]
Prosperi, M., Guo, Y., Sperrin, M., Koopman, J.S., Min, J.S., He, X., Rich, S., Wang, M., Buchan, I.E., and Bian, J., 2020. Causal inference and counterfactual prediction in machine learning for actionable healthcare. Nature Machine Intelligence 2, 7, 369-375.
[22]
Raihan, M., Mondal, S., More, A., Boni, P.K., and Sagor, M.O.F., 2017. Smartphone based heart attack risk prediction system with statistical analysis and data mining approaches. Advances in Science, Technology and Engineering Systems Journal 2, 3, 1815-1822.
[23]
Rault, T., Bouabdallah, A., Challal, Y., and Marin, F., 2017. A survey of energy-efficient context recognition systems using wearable sensors for healthcare applications. Pervasive and Mobile Computing 37, 23-44.
[24]
Roy, N., Pallapa, G., and Das, S.K., 2007. A middleware framework for ambiguous context mediation in smart healthcare application. In Third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2007) IEEE, 72-72.
[25]
Tsiouris, K.M., Gatsios, D., Rigas, G., Miljkovic, D., Seljak, B.K., Bohanec, M., Arredondo, M.T., Antonini, A., Konitsiotis, S., and Koutsouris, D.D., 2017. PD_Manager: an mHealth platform for Parkinson's disease patient management. Healthcare technology letters 4, 3, 102-108.
[26]
Verde, L., De Pietro, G., and Sannino, G., 2018. Voice disorder identification by using machine learning techniques. IEEE Access 6, 16246-16255.
[27]
World Health Organization, 2020. CardioVascularDiseases.
[28]
Yuan, B. and Herbert, J., 2014. Context-aware hybrid reasoning framework for pervasive healthcare. Personal and ubiquitous computing 18, 4, 865-881.

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cover image ACM Other conferences
DSAI '20: Proceedings of the 9th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion
December 2020
245 pages
ISBN:9781450389372
DOI:10.1145/3439231
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 09 June 2021

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  1. Disease Inference
  2. Mobile devices

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DSAI 2020

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Overall Acceptance Rate 17 of 23 submissions, 74%

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