Page 353 - Special Topic Session (STS) - Volume 4

STS1080 Fionn M.

Data science and the big data framework for
development, and to benefit from disruptive
technology advances
Fionn Murtagh
University of Huddersfield, UK

Abstract
Methodology is discussed and described here, as also are important data
sources, that can be very relevant also for sustainable development. At issue
is health and medical analytics, through analytical focus and contex-
tualization, with new challenges and opportunities in the context of Big Data,
Geometric Data Analysis: analytics of processes and behaviours. Foundational
here is the geometry and topology of data and information for analytics of
processes and behaviours. Also important is “homology” (i.e. associations that
are determined in integrated data sources) and “field” (i.e. analytical focus) of
eminent sociologist, Pierre Bourdieu, and addressing new societal challenges,
and new themes and topics, problems and challenges, in medicine and in
health and hence also in life sciences.

Keywords
Correspondence Analysis; Geometric Data Analysis; quantitative and
qualitative analytics; health and wellbeing; developing economy countries.

1. Introduction
First to be noted is how Big Data can be availed of, to form the context for
the patient’s treatment.
The first chapter of Zhang et al. (2018), pages 1–6, entitled “Big data and
clinical research: perspectives from a clinician” by Zhongheng Zhang,
counterposes, as research, interventional analysis, which is, in fact,
experimental research, relative to observational studies. An important release
of a “data sharing platform for population and health” in China in 2017 is
noted as: “historic leap in clinical research”.
An important point made is that patients’ treatments are usually
complicated by the patients comorbidities. So it becomes so very important
to have and to use ancillary and contextual observations also. This amounts to
the practical setting for big data clinical trials.
Electronic medical records can be very important. Such big data may very
well include also, demographic attributes, microbiology information on the
patient, and other data sources. So it is noted that such observational data,
encompassing what amounts to big data clinical trials, can be very relevant to
the real-world, i.e. detailed and comprehensive information on the patient.

342 | I S I W S C 2 0 1 9