Abstract
There is increasing interest in Big Data analytics in health care. Behavioral health analytics is a care management technology that aims to improve the quality of care and reduce health care costs based capture and analysis of data on patient’s behavioral patterns. Big Data analytics of behavioral health data offers the potential of more precise and personalized treatment as well as monitor population-wide events such as epidemics.
Mobile phones are powerful social sensors that are usually physically close to users and leave digital traces of users’ behaviors and movement patterns. New Apps (application or piece of software) are emerging that passively collect and analyze mobile phone data of at-risk patients such as their location, calling and texting records and app usage, and can find deviations in a user’s daily patterns to detect that something is wrong before an event occurs. Data mining and machine learning techniques are adopted to analyze the “automated diaries” created by the smart phone and monitor the well-being of people. The App first learns a patients daily behavioral patterns using machine learning techniques. Once trained, the App detects deviations and alerts carers based on predictive models.
This paper describes the techniques used and algorithms for reality mining and predictive analysis used in eHealth Apps.
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References
Chronis, I., Madan, A., Pentland, A.: SocialCircuits: the art of using mobile phones for modeling personal interactions. In: Proceedings of the ICMI-MLMI 2009 Workshop on Multimodal Sensor-Based Systems and Mobile Phones for Social Computing, Cambridge, Massachusetts, pp. 1–4 (2009)
Madan, A., Cebrian, M., Moturu, S., Farrahi, K., Pentland, A.: Sensing the “Health State” of a Community. IEEE Pervasive Computing 11(4), 36–45 (2012)
Madan, A., Cebrian, M., Lazer, D., Pentland, A.: Social sensing for epidemiological behavior change. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing, Copenhagen, Denmark, pp. 291–300 (2010)
Gundecha, P., Liu, H.: Mining Social Media: A Brief Introduction. Informs 9, 1–17 (2012)
Pentland, A.: Automatic mapping and modeling of human networks. Physica A: Statistical Mechanics and its Applications 378(1), 59–67 (2007)
Wlodarczak, P., Soar, J., Ally, M.: What the future holds for Social Media data analysis. World Academy of Science, Engineering and Technology 9(1), 545 (2015)
Big Data Gets Personal. MIT Technology Review 16(4) (2013)
Tretyakov, K.: Machine learning techniques in spam filtering. In: U. o. T. Institute of Computer Science (ed.) Data Mining Problem-Oriented Seminar, p. 19 (2004)
Witten, I.H., Frank, E., Hall, M.A.: Data Mining, 3rd edn. Elsevier, Burlington (2011)
Sookhanaphibarn, K., Thawonmas, R., Rinaldo, F., Chen, K.-T.: Spatiotemporal analysis in virtual environments using eigenbehaviors. In: Proceedings of the 7th International Conference on Advances in Computer Entertainment Technology, Taipei, Taiwan, pp. 62–65 (2010)
Eagle, N., Pentland, A.: Eigenbehaviors: identifying structure in routine. Behavioral Ecology and Sociobiology 63(7), 1057–1066 (2009)
Liu, B.: Sentiment Analysis and Opinion Mining. Morgan & Claypool (2012)
Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, 2nd edn. Springer, Heidelberg (2011)
Bai, Z., Wong, W.-K., Zhang, B.: Multivariate linear and nonlinear causality tests. Mathematics and Computers in Simulation 81(1), 5–17 (2010)
Diks, C., Panchenko, V.: A new statistic and practical guidelines for nonparametric Granger causality testing. Journal of Economic Dynamics and Control 30(9–10), 1647–1669 (2006)
Hiemstra, C., Jones, J.D.: Testing for Linear and Nonlinear Granger Causality in the Stock Price- Volume Relation. The Journal of Finance 49(5), 1639–1664 (1994)
Nolte, G., Ziehe, A., Krämer, N., Poupescu, F., Müller, K.-R.: Comparison of granger causality and phase slope index. In: NIPS 2008 Workshop on Causality, Canada (2008)
Simonite, T.: Smartphone Tracker Gives Doctors Remote Viewing Powers. Technology Review 116(4) (2013)
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Wlodarczak, P., Soar, J., Ally, M. (2015). Reality Mining in eHealth. In: Yin, X., Ho, K., Zeng, D., Aickelin, U., Zhou, R., Wang, H. (eds) Health Information Science. HIS 2015. Lecture Notes in Computer Science(), vol 9085. Springer, Cham. https://doi.org/10.1007/978-3-319-19156-0_1
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DOI: https://doi.org/10.1007/978-3-319-19156-0_1
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