Health Analytics and Big Data in Health
Kursthemen
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Learning objectives focus on the fundamental concepts of health analytics and big data. This will enable learners with full comprehension and critical attitude on the complex process of current data science field of work.
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Data plays a key role in modern industry and any organization.
Read about the use of big data and get the pre-knowledge for this topic!-
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As healthcare systems continue to adopt innovative technologies for different purposes, the volume of available data also continues to grow.
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Big Data addresses A collection of data sets that is so large and complex that it becomes difficult to process using hand database management tools or traditional data processing applications.
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Big data analytics covers integration of heterogeneous data, data quality control, analysis, modelling, interpretation, and validation.
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Artificial intelligence systems (AI) were defined as “software (and possibly also hardware) systems designed by humans ..."
Learn more about the definition of the European Commission! -
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The European Commission released a strategy for data governance and data policies in 2020. Read about basic facts!
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This chapter provides concepts and definitions in a graphic presentation.
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Big Data Applications (Jiang et al., 2017; Moghadam & Colomo-Palacios, 2018; Ristevski & Chen, 2018).
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Big Data and secondary data (Benchimol et al., 2015; Cheng & Phillips, 2014)
Secondary data is defined as data used for different purposes of the ones they were originally collect for. -
Information systems in Health (WHO, 2008)
According to WHO, “the health information system collects data from the health sector and other relevant sectors, analyses the data and ensures their overall quality, relevance and timeliness, and converts data into information for health-related decision-making.” -
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Example 1 – Example 5
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Here you see a case study for Big Data Application
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Analytical approaches can be defined in three categories, namely descriptive, predictive, and prescriptive.
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Computational methods for large databases (analytical and modelling techniques) (Magnuson J.A., 2020)
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During the process of designing and selecting the best model approach, there are few methods or approaches, namely: train and test, cross -validation and train, validation and test.
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During the process of designing and selecting the best model approach, there are few methods or approaches, namely: train and test, cross -validation and train, validation and test.
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Example 6 – In the following practical course, you will be able to understand how such concepts apply:
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For a hands-on practical code experience, you can visit the following open crash course:
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IBM defined data governance “as a discipline of quality control to add new rigor and discipline to the process of managing, using, improving and protecting organizational information”. Nevertheless, there is still no consensus for a proper definition of Data Governance.
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Here is the list of sources used for this module.
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