Abstract
QVida+ Project aims at continuously assessing the quality of life (QoL) of patients without requiring them to constantly fill QoL questionnaires. For this, QVida+ proposes to use a combination of sensor-based technology to acquire data on patients and data science (DS) algorithms to work this data in order to estimate the answers patients would give to the questionnaire items. One key decision of this project is the selection of the best data science tools to use. We compared different tools based on their popularity and support from the community, offered DS algorithms, price and licensing, and development environment. We concluded that R is the tool that best fits the purpose of our project, and we open the possibility for future use of Python in specific tasks of this project.
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Notes
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Data mining focuses on pattern extraction and knowledge extraction; machine learning focuses on algorithms that learn from past evidence and that are used as predictive techniques, classification and decision support; predictive analytics focuses on the use of statistical methods, such as regression analyses, to predict future values from past experience; and business intelligence focuses on presenting complex information to decision makers.
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Acknowledgments
This article is a result of the project QVida+: Estimação Contínua de Qualidade de Vida para Auxílio Eficaz à Decisão Clínica, NORTE‐01‐0247‐FEDER‐003446, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). The authors also acknowledge to the strategic project LIACC (PEst-UID/CEC/00027/2013).
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Urbano, J., Nogueira, P., Rocha, A.P., Cardoso, H.L. (2017). Analysis of Data Science Tools for Sensor-Based Assessment of Quality of Life in Health Care. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Costanzo, S. (eds) Recent Advances in Information Systems and Technologies. WorldCIST 2017. Advances in Intelligent Systems and Computing, vol 569. Springer, Cham. https://doi.org/10.1007/978-3-319-56535-4_45
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