Table Of ContentStudies in Big Data 68
Sujata Dash · Biswa Ranjan Acharya ·
Mamta Mittal · Ajith Abraham ·
Arpad Kelemen Editors
Deep Learning
Techniques
for Biomedical
and Health
Informatics
Studies in Big Data
Volume 68
Series Editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
Theseries“StudiesinBigData”(SBD)publishesnewdevelopmentsandadvances
in the various areas of Big Data- quickly and with a high quality. The intent is to
coverthetheory,research,development,andapplicationsofBigData,asembedded
inthefieldsofengineering,computerscience,physics,economicsandlifesciences.
The books of the series refer to the analysis and understanding of large, complex,
and/or distributed data sets generated from recent digital sources coming from
sensorsorotherphysicalinstrumentsaswellassimulations,crowdsourcing,social
networks or other internet transactions, such as emails or video click streams and
other. The series contains monographs, lecture notes and edited volumes in Big
Data spanning the areas of computational intelligence including neural networks,
evolutionary computation, soft computing, fuzzy systems, as well as artificial
intelligence, data mining, modern statistics and Operations research, as well as
self-organizing systems. Of particular value to both the contributors and the
readership are the short publication timeframe and the world-wide distribution,
which enable both wide and rapid dissemination of research output.
**Indexing:Thebooksofthisseriesaresubmitted toISIWebofScience, DBLP,
Ulrichs, MathSciNet, Current Mathematical Publications, Mathematical Reviews,
Zentralblatt Math: MetaPress and Springerlink.
More information about this series at http://www.springer.com/series/11970
Sujata Dash Biswa Ranjan Acharya
(cid:129) (cid:129)
Mamta Mittal Ajith Abraham
(cid:129) (cid:129)
Arpad Kelemen
Editors
Deep Learning Techniques
for Biomedical and Health
Informatics
123
Editors
Sujata Dash Biswa RanjanAcharya
Department ofComputer Science Schoolof Computer Science
NorthOrissa University andEngineering
Takatpur,Odisha, India KIIT Deemed toUniversity
Bhubaneswar, Odisha, India
Mamta Mittal
Computer Science andEngineering Ajith Abraham
Department ScientificNetwork forInnovation
G.B.PantGovernmentEngineeringCollege andResearch Excellence
NewDelhi, Delhi, India MachineIntelligence Research Labs
Auburn,AL, USA
Arpad Kelemen
Department ofOrganizational Systems
andAdultHealth
University of Maryland
Baltimore, MD,USA
ISSN 2197-6503 ISSN 2197-6511 (electronic)
Studies in BigData
ISBN978-3-030-33965-4 ISBN978-3-030-33966-1 (eBook)
https://doi.org/10.1007/978-3-030-33966-1
©SpringerNatureSwitzerlandAG2020
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Preface
Overview
Biomedical and Health Informatics is an emerging field of research at the inter-
section of information science, computer science, and health care. Health care
informatics and analytics is a new era that brings tremendous opportunities and
challengesduetoeasilyavailableplentyofbiomedicaldataforfurtheranalysis.The
aim of healthcare informatics is to ensure the high-quality, efficient healthcare,
bettertreatmentandqualityoflifebyefficientlyanalyzingofabundantbiomedical,
and healthcare data including patient’s data, electronic health records (EHRs) and
lifestyle.Earlier,itwascommonrequirementtohaveadomainexperttodevelopa
model for biomedical or healthcare; however, recent advancements in representa-
tionlearningalgorithms(deeplearningtechniques)allowtoautomaticallylearning
thepatternandrepresentationofthegivendataforthedevelopmentofsuchmodel.
Deep learning methods with multiple levels of representation in which at each
level the system learn higher abstract level representation. Deep learning based
algorithms has demonstrated great performance to a variety of areas including
computervision,imageprocessing,naturallanguageprocessing,speechrecognition,
video analysis, biomedical and health informatics etc. Deep learning approaches
suchasneuralnetworkssuchasdeepbeliefnetwork,convolutionalneuralnetwork,
deep auto-encoder, and deep generative networks have emerged as powerful com-
putational models. These have shown significant success in dealing with massive
data for large number of applications due to their capability to extract complex
hidden features andlearn efficient representation inunsupervised setting.
The book will play a vital role in improvising human life to a great extent. All
the researchers and practitioners those who are working in field of biomedical and
healthinformatics,anddeeplearningwillbehighlybenefited.Thisbookwouldbe
agoodcollectionofstate-of-the-artapproachesfordeeplearningbasedbiomedical
andhealthrelatedapplications.Itwillbeverybeneficialforthenewresearchersand
practitioners working in the field to quickly know the best performing methods.
They would be able to compare different approaches and can carry forward their
v
vi Preface
research in the most important area of research which has direct impact on bet-
termentofthehumanlifeandhealth.Thisbookwouldbeveryusefulbecausethere
is no book in the market which provides a good collection of the state-of-the-art
methods of deep learning based models for biomedical and health informatics as
Deep learning is recently emerged and very un-matured field of research in
biomedical and healthcare.
This book, Deep Learning Techniques for Biomedical and Health Informatics,
aims topresent discussionson variousapplications ofdeep learningrelating tothe
Biomedical and Health Informatics problems and suggest latest research method-
ologies and emerging developments to benefit the researchers and practitioners. In
thisvolume,49researchersandpractitionersofinternationalreputehavepresented
latestresearchdevelopments,currenttrends,stateoftheartreports,casestudiesand
suggestions for further development in the field of biomedical and health infor-
matics, and deep learning.
Objective
The purpose of this book is to report the latest advances and developments in the
field ofbiomedical and healthinformatics, and deep learning. The book comprises
the following three parts:
(cid:129) Deep Learning for Biomedical Engineering and Health Informatics
(cid:129) Deep Learning and Electronics Health Records
(cid:129) Deep Learning for Medical Image Processing
Organization
There are 16 chapters in Deep Learning Techniques for Biomedical and Health
Informatics. They are organized into three parts, as follows:
(cid:129) Part One: Deep Learning for Biomedical Engineering and Health Informatics.
This part has a focus on deep learning paradigms and its application in
biomedical and health informatics, clinical decision support systems, disease
diagnosis and monitoring systems and recommender systems for health infor-
matics. There are six chapters in this part. The first chapter looks into the
application of deep learning to healthcare data in the task like information and
relation extraction. The second and third contribution focus on discovery of
biomedical named entities from many biomedical text mining task applying
deep learning techniques. The fourth chapter introduces deep learning and
developmentsinneuralnetworkandthendiscussesitsapplicationsinhealthcare
Preface vii
anditsrelevanceinbiomedicalinformatics andcomputationalbiologyresearch
in public health domain. The fifth chapter discusses various existing deep
learning techniques and their applications for decision support in clinical sys-
tems. The sixth chapter discusses the challenges and issues of health recom-
mender system.
(cid:129) Part Two: Deep Learning and Electronics Health Records. The second part
comprises seven chapters. The first contributiondiscusses about thedesign and
implementation of explainable deep learning system for healthcare using HER.
The second chapter audits the deep learning strategies connected with EHR
informationexaminationandinduction.Thethirdchaptercontributionfocuson
the extensive application of deep learning in many domains, including bioin-
formatics for the analysis and classification of biomedical imaging data,
sequencedatafromomicsandbiomedicalsignalprocessing.Thefourthchapter
discussesadvanceddistributedsecuritytechniquessuchasblockchaintoprotect
the health data from unauthorized access and the fifth contribution presents
CNN based classification for malaria disease to classify the blood films into
infectedandnormalblood films.Thesixthchapterpresentsdeepreinforcement
learning based approach for complete health care recommendations including
medicines to take, doctors to consult, nutrition to acquire and activities to
perform that consists of exercises and preferable sports. The seventh contribu-
tionpresentstheadvantagesindealingwithtext-basedextractionsandretrievals
using deep learning techniques.
(cid:129) Part Three: Deep Learning for Medical Image Processing. There are three
chapters in this part. The first chapter discusses several deep learning archi-
tectures which can be effectively used for HRV signal analysis for the purpose
ofdetectionofdiabetes.Thesecondchapterdiscussestheissuesandchallenges
of DL approaches for analysing biomedical images and its application for
classification,registrationandsegmentation.Thelastchaptergivesanoverview
ofdeeplearning-basedsegmentationalgorithmswithaspecialreferencetobrain
tumor classification, various challenges, along with its future scope.
Target Audiences
The current volume is a reference text aimed to support a number of potential
audiences, including the following:
(cid:129) Researchers in this field who wish to have the up-to-date knowledge of the
current practice, mechanisms, and research developments.
(cid:129) Students and academicians of biomedical and informatics field who have an
interest in further enhancing the knowledge of the current developments.
viii Preface
(cid:129) Industry and peoples from Technical Institutes, R&D Organizations, and
workinginthefieldofmachinelearning,deeplearning,biomedicalengineering,
health informatics, and related fields.
Baripada, Odisha, India Sujata Dash
Bhubaneswar, Odisha, India Biswa Ranjan Acharya
New Delhi, India Mamta Mittal
Auburn, AL, USA Ajith Abraham
Baltimore, MD, USA Arpad Kelemen
Acknowledgements
The editors would like to acknowledge the help of all the people involved in this
projectand,morespecifically,tothereviewerswhotookpartinthereviewprocess.
Without their support, this book would not have become a reality.
First, the editors would like to thank each one of the authors for their time,
contribution, and understanding during the preparation of the book.
Second, the editors wish to acknowledge the valuable contributions of the
reviewers regarding the improvement of quality, coherence, and content presenta-
tion of chapters.
Lastbutnotleast,theeditorswishtoacknowledgethelove,understanding,and
support of their family members during the preparation of the book.
Baripada, Odisha, India Sujata Dash
Bhubaneswar, Odisha, India Biswa Ranjan Acharya
New Delhi, India Mamta Mittal
Auburn, AL, USA Ajith Abraham
Baltimore, MD, USA Arpad Kelemen
ix
