Introduction to the special issue on social data analytics in medicine and healthcare

Rodríguez‐González, Alejandro; Vakali, Athena; Mayer, Miguel Angel; Okumura, Takashi; Rodríguez-González, Alejandro; Spiliopoulou, Myra

doi:10.1007/s41060-019-00199-9

Cited by 4 publications

(5 citation statements)

References 5 publications

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“…And according to the Twitter Terms, by submitting, posting or displaying content on Twitter, the user grants Twitter a worldwide full-fledged copyright licence with the right to sublicence 27 and Twitter by releasing the Twitter APIs to developers grants the licence to integrate Twitter content into the services or conduct analysis of such Twitter content, but subject to the Terms of Twitter. 28 The management of IPR both as sharing of IP-protected materials in a legally compliant way requires legal and technical solutions. The integration of such solutions into SoBigData RI is discussed in the subsequent sections next.…”

Section: Intellectual Property Lawmentioning

confidence: 99%

“…In the last years, we have witnessed different initiatives in Europe aimed at providing environments and infrastructures to share research data and technologies, in accordance with the principles of Open Research Data and Open Science. The general idea of these initiatives is to provide ecosystems for enhancing scientific collaborations among researchers and our movements [15], healthcare [28], our social interactions and emotions [13].…”

Section: Introductionmentioning

confidence: 99%

“…d4science.org/group/sobigdata-gateway/data-catalogue.27 Article 3 Twitter Terms of Service. Available at https://twitter.com/ en/tos 28. Paragraph I B i licence from Twitter, Twitter Developer Agreement, Effective: May 25, 2018.…”

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confidence: 99%

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An ethico-legal framework for social data science

Forgó

Hänold

Hoven

et al. 2020

Int J Data Sci Anal

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Section: Intellectual Property Lawmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An ethico-legal framework for social data science

Forgó

Hänold

Hoven

et al. 2020

Int J Data Sci Anal

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“…In the field of health, machine learning and big data analytics have been useful in predicting diseases such as diabetes [54,9,109,110]. Health sectors have voluminous and huge datasets that can be vital in machine learning [110]. However, generating datasets for poverty is time-consuming, costly, and requires various multiple thresholds.…”

Section: Review Of Literaturementioning

confidence: 99%

Measuring the Unmeasurable through Machine Learning Regressions and Classifications: Multidimensional Poverty Predictions in the Poorest Region of Luzon, Philippines

Onsay,

Rabajante

2024

Preprint

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Poverty is notoriously difficult to quantify, it is multidimensional and is deemed unmeasurable in the field of social science. Current poverty measurements are time-consuming, labor-intensive, and cost-expensive. Hence, policy targeting becomes challenging for policymakers to implement poverty alleviation programs. Thus, this work proposes new measures of poverty in the poorest region of Luzon, Philippines by training and testing the community-based system datasets. We have utilized machine learning regression and classification algorithms matched with advanced econometrics models. For regression, we applied 7 algorithms, for 273 ensemble runs, and for classification, we employed 12 algorithms, for 468 ensemble runs to analyze 34 locals, 4 sectors at disaggregation system, and then combined. Random forest regression outperforms all models with MSE(0.0792), RMSE(0.3298), and R-square(0.92075), while random forest classifier outperforms all models with the highest accuracy(91.08% at random and 95.95% at pipeline). It also validates the existing correlation and causation between multidimensional attributes (27 variables) and poverty outcomes (Incidence, gap, severity, and watts). This work highlights the feasibility of machine learning for poverty prediction that can minimize cost, reduce labor, and maximize time, particularly in the poorest regions of the Philippines. Finally, the output has provided policy targeting tools for poverty reduction for various locals at different poverty configurations.

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“…In the field of information and communications technology (ICT), a common goal is to achieve responsible research and innovation (RRI) aimed at providing a platform or ecosystem for ethics-sensitive scientific discoveries and advanced applications of social data mining on the various dimensions of social life, or, in other words, social big data science. More and more often, these data regard private aspects of our lives, such as our movements (Inkpen et al, 2018), healthcare (Rodríguez-González et al, 2019), our social interactions and our emotions (Hasan et al, 2019). In this context, it therefore becomes fundamental to take into consideration the legal and ethical aspects of the processing of personal data, especially given the entry into force of the General Data Protection Regulation (GDPR) in May 2018, but also to move forward considering already existing recommendations and exploring the frontier of novel solutions, in accordance with shared societal and moral values.…”

Section: Introductionmentioning

confidence: 99%