Abstract
Enhancing personalisation is important for productive collaboration between humans and machines. This is because the integration of human intelligence with cognitive computing would provide added value to healthcare. While the well-being and human health can be profoundly affected by weather, the effect of machine learning on personalised weather-based healthcare for self-management is unclear. This paper seeks to understand how machine learning use affects the personalisation of weather-based healthcare. Based on the Uses and Gratifications Theory (UGT), new constructs are incorporated (demography, weather and effectiveness) in order to propose a model for health science with machine learning use, weather-based healthcare, and personalisation. Subsequently, this paper proposes building a system that can predict the symptoms of two diseases (asthma and eczema) based on weather triggers. The outcome from this paper will provide deeper understanding of how personalisation is impacted by machine learning usage and weather-based healthcare for individual patients’ self-management and early prevention. The findings in this paper will also assist machine learning facilitators design effective use policies for weather-based healthcare that will have new fundamental knowledge with personalisation to enhance the future of intelligent health informatics, and artificial intelligence.
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References
Akhil, J., Samreen, S., Aluvalu, R.: The future of healthcare: machine learning. Int. J. Eng. Technol. (UAE) 7, 23–25 (2018)
Panch, T., Szolovits, P., Atun, R.: Artificial intelligence, machine learning and health systems. J. Glob. Health 8(2), 020303 (2018). https://doi.org/10.7189/jogh.08.020303
Lepeule, J., Litonjua, A.A., Gasparrini, A., Koutrakis, P., Sparrow, D., Vokonas, P.S., Schwartz, J.: Lung function association with outdoor temperature and relative humidity and its interaction with air pollution in the elderly. Environ. Res. 165, 110–117 (2018)
Alharbi, E., Abdullah, M.: Asthma attack prediction based on weather factors. Periodicals Eng. Nat. Sci. 7, 408–419 (2019). https://doi.org/10.21533/pen.v7i1.422
AAFA.: Weather can trigger asthma. Asthma and Allergy Foundation of America (2017). https://www.aafa.org/weather-triggers-asthma. Accessed 6 Aug 2020
D'Amato, G., Pawankar, R., Vitale, C., Lanza, M., Molino, A., Stanziola, A., Sanduzzi, A., Vatrella, A., D'Amato, M.: Climate change and air pollution: Effects on Respiratory Allergy. Allergy, Asthma Immunol. Res. 8(5), 391–395 (2016) https://doi.org/10.4168/aair.2016.8.5.391
Zhang, Y., Peng, L., Kan, H., Xu, J., Chen, R., Liu, Y., Wang, W.: Effects of meteorological factors on daily hospital admissions for asthma in adults: A time-series analysis. PLoS One 9(7), e102475 (2014). https://doi.org/10.1371/journal.pone.0102475
Balato, N., Megna, M., Ayala, F., Balato, A., Napolitano, M., Patruno, C.: Effects of climate changes on skin diseases. Expert Rev. Anti. Infect. Ther. 12, 171–181 (2014)
Vocks, E., Busch, R., Frölich, C., Borelli, S., Mayer, H., Ring, J.: Influence of weather and climate on subjective symptom intensity in atopic dermatitis. Int. J. Biometeorol. 45, 27–33 (2001)
Charman, C., Venn, A., Ravenscroft, J., Williams, H.: Translating patient-oriented eczema measure (POEM) scores into clinical practice by suggesting severity strata derived using anchor-based methods. Br. J. Dermatol. 169(6), 1326–1332 (2013)
POEM. Patient Oriented Eczema Measure (2020). https://www.nottingham.ac.uk/research/groups/cebd/resources/poem.aspx. Accessed 6 Aug 2020
Scarpino, M.: Tensor Flow for Dummies. Wiley, Hoboken, pp. 8–43, pp. 201–224, USA (2018)
Kurata, J.: Understanding machine learning with python (2016). Pluralsight. https://app.pluralsight.com. Accessed 30 Apr 2020
Bassi, S.: Python for Bioinformatics. 2nd ed. CRC Press, Taylor & Francis Group, Boca Raton, pp. 30–37, pp. 158–208 (2018)
Tsang, K., Pinnock, H., Wilson, A., Shah, S.: Application of machine learning to support self-management of asthma with mHealth. In: 42nd Annual Int’l Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada (2020)
Gaynor, M., Schneider, D., Seltzer, M., Crannage, E., Barron, M.L., Waterman, J., Obersle, A.: A user‐centered learning asthma smartphone application for patients and providers. Learn. Health Syst. 4(3), e10217 (2020)
Hurault, G., Domínguez‐Hüttinger, E., Langan, S.M., Williams, H.C., Tanaka, R.J.: Personalized prediction of daily eczema severity scores using a mechanistic machine learning model. Clin. Exp. Allergy (2020). https://doi.org/10.1111/cea.13717
Wunderground. Weather Underground Application Programming Interface (API) (2020). https://www.wunderground.com/weather/api/d/pricing.html. Accessed 6 Aug 2020
DarkSky. DarkSky API (2020). https://darksky.net/dev/docs. Accessed 6 Aug 2020
VanderPlas, J.: Python data science handbook. O’Reilly Media Inc, Sebastopol (2017)
Geron, A.: Hands-on machine learning with scikit-learn and tensorflow. O’Reilly Media Inc, Sebastopol (2017)
Hossain, M.: Effects of uses and gratifications on social media use. PSU Res. Rev. 3(1), 16–28 (2019). https://doi.org/10.1108/prr-07-2018-0023
Phan, D., Yang, N., Kuo, C., Chan, C.: Deep learning approaches for sleep disorder prediction in an asthma cohort. J. Asthma (2020) https://doi.org/10.1080/02770903.2020.1742352
Acknowledgment
The authors appreciate the financial support given by the Fundamental Research Grant Scheme, FRGS/1/2019/SS06/MMU/02/4 and Multimedia University, Cyberjaya, Malaysia (Project ID: MMUE/190031).
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Haque, R. et al. (2021). Intelligent Health Informatics with Personalisation in Weather-Based Healthcare Using Machine Learning. In: Saeed, F., Mohammed, F., Al-Nahari, A. (eds) Innovative Systems for Intelligent Health Informatics. IRICT 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-030-70713-2_4
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