3. TopicCover
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◍ Introduction.
◍ What is Deep Learning?
◍ Deep Learning Architectures.
◍ Architectures Uses.
◍ Deep Learning for Healthcare.
◍ Challenges.
◍ Conclusion.
4. “
Deep-learning will transform every single industry. Healthcare
and transportation will be transformed by deep-learning. I want to
live in an AI-powered society. When anyone goes to see a
doctor, I want AI to help that doctor provide higher quality and
lower cost medical service. I want every five-year-old to have a
personalized tutor.
--Andrew Ng
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5. Introduction
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◍ With a massive influx of multimodality data, the role of data analytics in
health informatics has grown rapidly in the last decade. The prevalence
of big data in healthcare has paved the way for applications based on
deep learning techniques in the past few years. Applications of
machine learning in healthcare range from disease prediction to
patient-level personalized services.
6. ◍ WhatIs DeepLearning?
◍ Deep learning is an artificial intelligence(AI) function that imitates the
workings of the human brain in processing data and creating patterns
for use in decision making. Deep learning is a subset of machine
learning in artificial intelligence that has networks capable of learning
unsupervised from data that is unstructured or unlabeled. Also known
as deep neural learning or deep neural network.
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13. DEEPLEARNINGFORHEALTHCARE
◍ Disease Prediction: Machine learning
has been instrumental in disease prediction
and pattern analysis of health informatics data.
In the past few years, deep learning has
surpassed all benchmarks especially where
analyzing patterns in raw data or images is
required. The problem of predicting diseases
based on a patient’s attribute falls directly
under the scope of deep learning
methodologies where the objective is to learn
and replicate the decision making capability of
a medical practitioner as accurately as
possible.
◍ Data visualization applications: A lot
of healthcare data is in the form of images,
which arise from various diagnostic procedures
such as Magnetic Resonance Imaging (MRI),
radiography, ultrasound, thermography,
tomography, functional near-infrared
spectroscopy (fNIRS), among others. Deep
learning approaches play a very significant role
in enabling analysis and visualization of such
data. Data visualization applications in the
healthcare domain is the development of
executive level summary information systems
that mine large health databases to extract
information that is relevant to future decision
making.
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14. DEEPLEARNINGFORHEALTHCARE
◍ Assistive technology solutions:
Assistive technology is an area of research in
itself, but with the advent of wearable
technologies and Internet of Things (IoT), there
is a rising interest in providing personalized
experience by utilizing data from EHRs and
Health Informatics solutions. Machine learning
for assistive technologies constitute
applications such as Brain Computer
Interfaces (BCI), neural prosthesis,
rehabilitative devices and others which aid
persons with disabilities by the use of
biomedical signal processing and artificial
intelligence via machine learning
◍ Security and Privacy of Health
Records: Security of health records is an
important and challenging problem. Healthcare
data is available on cloud, mobile devices and
other platforms in the form of EHR, Personal
Health Records (PHR) etc. A recent study in
United Kingdom shows that a majority of
individuals are concerned about the privacy of
their health records. Moreover, the increasing
sophistication of attacks and criticality of data
mandates for more robust and intelligent
techniques.
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18. Challenges
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◍ One of the perspectives of analyzing challenges is from a machine learning
standpoint. This includes the limitations faced by conventional machine
learning techniques in their application to health informatics. A primary
problem stems from the lack of training samples for the learning model. For
example, the conventional disease detection model, using a Support Vector
Machine (SVM) classifier for instance, would fail to generalize well on the
dataset as the number of positive samples is relatively much lower than those
compared to negative samples. This issue also arises in the case of multiintent
classification for assistive technology development.
19. Challenges
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◍ Data pre-processing for applying learning algorithms also presents a
significant challenge. Representation of appropriate feature spaces for data
and feature engineering are often major application-specific challenges. The
anonymization of patient data is also important, which has been explored in.
◍ Another problem that arises is the number of dimensions of data available for
analysis.
◍ There is also a concern regarding the reliability of the results arrived at by the
machine learning techniques on healthcare datasets.
20. Conclusion
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◍ Based on the recently emerging trends, it is certain that deep learning
methodologies hold significant promise in developing intelligent
applications for healthcare and health informatics domains owing to
the prevailing nature of data. Evolving deep learning network
architectures that have low computational, memory and power
footprint are desired for emerging applications, which not only process
data from healthcare systems, but also from a wide variety of devices
and sensors that endow an individual and an implicit connected
environment. The evolving Internet of Things (IoT) and healthcare
integrated applications appear to be the primary consumers of
evolving trends in deep learning.
