The second process of refining the original Attitude system occurred during the application of the adapted framework to analyse the main study data, which resulted in a second set of modifications of the original Attitude system.
Patient care and family support are rated lower than the main comparative data which pertains to US hospices [ 18, 19] but the fact that acute hospitals deal with a wider spectrum of deaths than hospices, from sudden to expected, would need to be taken into account in any valid comparison [ 29].
As it will be shown later on in this paper, there are two main data streams which need to be analyzed for this: (1) location data being uploaded to our server and (2) content updates (i.e. event specific information such as schedules, maps, etc).
Our approach utilized three main data sets which encompassed two separate patient cohorts and three distinct gene expression measurement platforms.
In this review, however expert opinion was a main source of data, which makes the validity of conclusions difficult to ascertain.
More generally, the main characteristics of the data which led to our approach are non-stationary landmark movements and non-discriminative local texture information of certain landmarks (cf. Subsection 1.2).
Moreover, we can consider that the size of datasets that qualify as big data may keep on increasing due to the main property of big data, which is the challenge on data processing and the fact that computational infrastructure that is required to process these large-scale datasets may progress with time.
Steps 2 and 3 constitute the main data pool on which this article is based.
Initially, scientific biomedical articles were the main data source, after which research on clinical notes, blogs and patient forums, etc. has also increased.
Four main data sources from which sub-studies derive their data comprise representative US patient populations with kidney disease that are followed through active and passive means until death.
