Descriptive, Predictive, and Prescriptive Analytics
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Advances in techniques for predictive and prescriptive data analytics and increased availability of data via IoT devices is shifting IoT to analytics-driven actuation.
“Such advances in techniques for conducting predictive and prescriptive data analytics and increased availability of data via the IoT devices is shifting IoT to analytics-driven actuation i.e.IoT application are increasingly being developed to extract high value knowledge from the IoT data that allows them to take actions on behalf of humans [8].”
From QoE in IoT: a vision, survey and future directions (2021) · cites this paper as [8]
Healthcare analytics that allow collection of objective data, such as app or website use, to predict healthcare outcomes using machine learning approaches constitute an approach that has been overlooked so far in online mental health interventions.
“Finally, healthcare analytics that allow the collection of objective data [59–61], such as app or website use, to predict healthcare outcomes using machine learning approaches [60, 62] constitute an approach that has been overlooked so far in online mental health interventions.”
From Effectiveness of online mindfulness interventions on medical students’ mental health: a systematic review (2021) · cites this paper as [62]
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QoE in IoT: a vision, survey and future directions
Fizza, K., Banerjee, A., Mitra, K., Jayaraman, P. P., Ranjan, R., Patel, P., & Georgakopoulos, D. (2021) · Discover Internet of Things · doi:10.1007/s43926-021-00006-7 · Cited by 107 · see citing works
“…and prescriptive data analytics and increased availability of data via the IoT devices is shifting IoT to analytics-driven actuation i.e. IoT application are increasingly being developed to extract high value knowledge from the IoT data that allows them to take actions on behalf of humans [8].”
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- Authors
- El Morr, C., & Ali-Hassan, H.
- Venue
- SpringerBriefs in Health Care Management and Economics
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 31-55
- ISSN
- 2193-1704 (ISSN-L)
- 2193-1712
- OpenAlex
- W2915058771
- Cited by
- 35
- References
- 17
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 4 · 2023: 6 · 2022: 8 · 2021: 6 · 2019: 1 · 2016: 1
- FWCI
- 18.64
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- SpringerBriefs in health care management and economics (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- descriptive analytics, predictive analytics, prescriptive analytics, data mining, machine learning, artificial intelligence, supervised learning, unsupervised learning, multidimensional data, data extraction
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