Machine Learning in the Era of Digital Health

MACHINE LEARNING IN THE ERA OF DIGITAL HEALTH
ABSTRACT
Digital health can be defined as the great lean of this present generation. This technology has
linked together the various edges of care in order to ensure that health information is
maintained regularly. This would help in improving the health care system and also improve
the delivery services. Similar to this the concept of machine learning related to healthcare is
considered as the major agent for the delivery of smart healthcare. Besides numerous
advantages it also has many disadvantages, these are still being resolved. This has been
calling for a resolution that is up thrust because the healthcare system has been growing
digital continuously.
The main objective of this paper is to research about the interaction of machine learning with
the sector of digital health along with that, we would present the issues in machine learning
from the concept of digital health. We would also identify the recent as well as future roles of
the machine learning within the system of digital health.
This particular research work would also look into the analysis of the acceptance rate of
machine learning in the digital health around various stakeholders of healthcare. This
vantage is very important for comprehending equality within the recent decrement properly.
The decrement is about how intelligent as well as linked would be the system of future health
would actually appear. The topic of research would also undergo a particular comprehensive
research in predictable future for the aspects of artificial and computational intelligence
methods that are applicable to the digital health and algorithm,
KEY WORDS: Machine learning, Healthcare, Service delivery, Digital Health,.
INTRODUCTION
Digital health can be known as the epic
quantum of technology generation of the
recent leap (Samuel, 1959). It consists of
linking the edges of care in order to ensure
that the health information is disseminated
resolutely. This linking is done
electronically and it can help in improving
the healthcare services as well as improve
the delivery (Steinhubl, 2013). In this
recent era the machine learning has
become opposite with the digital health, it
does not withstand the actual threat to

machine learning (Samuel, 1959). This can
be called for a rapid attention because
there are numerous avalanches of
algorithms that produce from numerous
digital healths. Digital health as well as
machine learning proves to be more
acceptable. It has been a very important
key word in the field of healthcare
(Goodfellow & Yoshua, 2016). The
particular system lacks adequate
perception of using the computer systems
in order to perform the tasks which
actually need human intelligence. This
utilization of the digital health is very
spontaneous (Greenspan, Hayit & Bram,
2016). The data and algorithms received
from the other devices that are related lead
copious, intricacy and convoluted
algorithms (Samuel, 1959). This vast
change is instigated because of the
increasing trend of using various internet
devices, access to the internet, numerous
devices that can be worn as well as smart
phones that are generated by the
applications and devices (Jordan, Michael
& Tom 2015). As an effect, there are
various ways to forward as well as
improve the conduct, enterprise aggregate
and advance of the data set as well as an
algorithm (Qureshi, 2014). And in
addition, how to interpret and utilise them
to better fitness care transport that will
favour,
the purchaser (patient), health practitioners
, and health stakeholders (Carlos, Ruth &
Charles 2018). The modern-
day innovation by means of some agency
like Mayo-clinic, IBM Watson Health,
Google (Deep
Mind) towards evolving advanced digital f
itness care gadget is considerable to these
trends (Shen, Dinggang & Guorong 2017).
For better understanding, it is very
important to the elementary and authentic
formation of digital health (Qureshi,
2014). Digital health can be defined as the
integration of health with various digital
technologies (Chekroud, Adam & Ryan,
2016). It also helps the society to enhance
the actual effectiveness of healthcare
delivery and makes the manufacturing of
medicines more precise and personalized
(Bhavnani, et al., 2016). This discipline
includes the usage of communication and
information technologies in order to help
to find out the health problems as well as
challenges that patients face (Bhavnani, et
al., 2016). The technologies are in the
form of software or hardware and the
services nd solutions like telemedicine,
email, applications and mobile phones,
text messages, remote or clinic monitoring
sensors and wearable devices (Widmeret
al., 2015). The main interest of the digital
health of about improving the health
systems that are inter related that would in
return improve the usage of smart devices,

computational techniques as well as
communication media for aiding the
healthcare professionals and patients to
manage the health risks (Obermeyer, Ziad,
& Ezekiel 2016). It also promotes good
health and wellbeing.
The unexpected and very fast acceptance
level of the digital health is increases by
the innovations brought about in the three
pillars of technology of digital health
(Erickson, Bradley, Panagiotis 2017).
These pillars include medical sensing,
computing and telecommunications. This
has been shown in the figure below. The
changes that have been brought about by
the technologies of digital health
especially the technologies that is new and
associated with Web 2.0 innovations
(Qureshi, 2014).
Most publications have been written from
a generally uncritical preventive clinical or
health promoting point of view and
undertake a techno-utopian perspective,
tending to laud the possibilities of these
technologies besides acknowledging the
social, ethical and political implications of
their use (Cabitza, Federico, Raffaele
2017). From a necessary sociological
perspective, however, an extra in-depth
and nuanced analysis may be undertaken
of how these technologies may
additionally function to construct a number
of forms of subjectivities and
embodiments and participate in the
configuring and replica of energy
relations. Digital health is a multi-
disciplinary domain which entails many
stakeholders, including clinicians,
researchers and scientists with an
extensive range of expertise in healthcare,
engineering, social sciences, public health,
health economics and management (Lu,
Chia-Feng & Fei-Ting 2018). The
functionality of future digital fitness
systems to translate and successfully
radically change this lack of actionable
facts to a meaningful one stays one of the
key challenges in creating smarter more
personalized and efficient digital Health
shipping systems (Shen, Dinggang &
Guorong 2017).
The fact that medical field uses numerous
process implies that using human labor is
safer than using computers for several
purposes (Holzinger, Andreas & Igor
Jurisica, 2014). Since around a decade the
advancement in using electronic
appliances for medical care has been
advancing. The fact that should be
considered in this case is that the data
provided by technologies is not better than
the previously used charts that have been
replaced (Shen, Dinggang & Guorong
2017). If the technology aims in improving
care in the future time, then the data
provided to the healthcare professionals

should be enhanced with help of the power
of machine learning and analytics. The
usage of these types of advances analytics,
doctors can be provided with better data
during the patient care (Shen, Dinggang &
Guorong 2017). It would help the
professionals in having easy access to vital
signs like blood pressure. Clinicians need
more information which would be useful
for them in making better decisions
regarding the treatments as well as
diagnosis of the patients (Shen, Dinggang
& Guorong 2017). It also helps them to
estimate the treatments that can be
undergone by the patients and the cost
incurred in those treatments. The actual
use of machine learning in the field of
healthcare is its capacity to process large
datasets which is not possible in the scope
of human labour (Shen, Dinggang &
Guorong 2017). After the data has been
collected its analysis has been carried out
and its results are converted into numerous
clinical insights which help the physicians
in proving care to patients with proper
planning (Shen, Dinggang & Guorong
2017). This would ultimately lead in good
outcomes along with fewer costs incurred
in it. It also increases the satisfaction of
patients. It has been said before that the
tool of machine learning which proves to
be best in the medicine field is the brain of
the doctor. This sometimes leads in
healthcare professionals’ judge the tools of
machine learning as less wanted (Shen,
Dinggang & Guorong 2017). Usage of
machine learning also creates fear of
unemployment within the professionals. In
the same way numerous physicals bear the
fear that machine learning is considered as
the starting of a process which would
make them obsolete but it should not be
forgotten that physicians in the field of
medicine can never be forgotten or
replaced. Patients would require human
touch and care along with a compassionate
relation with the professionals who deliver
care (Shen, Dinggang & Guorong 2017).
Machine learning as well as any future
technology would fail in proving the
affection and care to the patients that are
given by human physicians (Shen,
Dinggang & Guorong 2017). They can just
be used as tools that would help the
professionals in improving the care
provided to the patients. The main focus of
professionals should be in the ways by
which machine learning can be used in
order to augment patient care (Shen,
Dinggang & Guorong 2017). An example
of this is suppose a professional is testing a
patient for a dangerous disease called
cancer, then the professional would require
t he best quality of results obtained from
the biopsy. Machine earning can be used
in reviewing the slides of pathology and
assist the physician with a specific
diagnosis (Shen, Dinggang & Guorong