Deep Learning Techniques for Biomedical and Health Informatics PDF
Preview Deep Learning Techniques for Biomedical and Health Informatics
Studies 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 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodologynowknownorhereafterdeveloped. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained hereinorforanyerrorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregard tojurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland 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
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