Deep Learning Techniques for Biomedical and Health Informatics

✍ Scribed by Sujata Dash, Biswa Ranjan Acharya, Mamta Mittal, Ajith Abraham, Arpad Kelemen

Publisher: Springer International Publishing
Year: 2020
Tongue: English
Leaves: 395
Series: Studies in Big Data 68
Edition: 1st ed. 2020
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

This book presents a collection of state-of-the-art approaches for deep-learning-based biomedical and health-related applications. The aim of healthcare informatics is to ensure high-quality, efficient health care, and better treatment and quality of life by efficiently analyzing abundant biomedical and healthcare data, including patient data and electronic health records (EHRs), as well as lifestyle problems. In the past, it was common to have a domain expert to develop a model for biomedical or health care applications; however, recent advances in the representation of learning algorithms (deep learning techniques) make it possible to automatically recognize the patterns and represent the given data for the development of such model.

This book allows new researchers and practitioners working in the field to quickly understand the best-performing methods. It also enables them to compare different approaches and carry forward their research in an important area that has a direct impact on improving the human life and health.

It is intended for researchers, academics, industry professionals, and those at technical institutes and R&D organizations, as well as students working in the fields of machine learning, deep learning, biomedical engineering, health informatics, and related fields.

✦ Table of Contents

Front Matter ....Pages i-xxv
Front Matter ....Pages 1-1
MedNLU: Natural Language Understander for Medical Texts (H. B. Barathi Ganesh, U. Reshma, K. P. Soman, M. Anand Kumar)....Pages 3-21
Deep Learning Based Biomedical Named Entity Recognition Systems (Pragatika Mishra, Sitanath Biswas, Sujata Dash)....Pages 23-40
Disambiguation Model for Bio-Medical Named Entity Recognition (A. Kumar)....Pages 41-55
Applications of Deep Learning in Healthcare and Biomedicine (Shubham Mittal, Yasha Hasija)....Pages 57-77
Deep Learning for Clinical Decision Support Systems: A Review from the Panorama of Smart Healthcare (E. Sandeep Kumar, Pappu Satya Jayadev)....Pages 79-99
Review of Machine Learning and Deep Learning Based Recommender Systems for Health Informatics (Jayita Saha, Chandreyee Chowdhury, Suparna Biswas)....Pages 101-126
Front Matter ....Pages 127-127
Deep Learning and Explainable AI in Healthcare Using EHR (Sujata Khedkar, Priyanka Gandhi, Gayatri Shinde, Vignesh Subramanian)....Pages 129-148
Deep Learning for Analysis of Electronic Health Records (EHR) (Pawan Singh Gangwar, Yasha Hasija)....Pages 149-166
Application of Deep Architecture in Bioinformatics (Sagnik Sen, Rangan Das, Swaraj Dasgupta, Ujjwal Maulik)....Pages 167-186
Intelligent, Secure Big Health Data Management Using Deep Learning and Blockchain Technology: An Overview (Sohail Saif, Suparna Biswas, Samiran Chattopadhyay)....Pages 187-209
Malaria Disease Detection Using CNN Technique with SGD, RMSprop and ADAM Optimizers (Avinash Kumar, Sobhangi Sarkar, Chittaranjan Pradhan)....Pages 211-230
Deep Reinforcement Learning Based Personalized Health Recommendations (Jayraj Mulani, Sachin Heda, Kalpan Tumdi, Jitali Patel, Hitesh Chhinkaniwala, Jigna Patel)....Pages 231-255
Using Deep Learning Based Natural Language Processing Techniques for Clinical Decision-Making with EHRs (Runjie Zhu, Xinhui Tu, Jimmy Huang)....Pages 257-295
Front Matter ....Pages 297-297
Diabetes Detection Using ECG Signals: An Overview (G. Swapna, K. P. Soman, R. Vinayakumar)....Pages 299-327
Deep Learning and the Future of Biomedical Image Analysis (Monika Jyotiyana, Nishtha Kesswani)....Pages 329-345
Automated Brain Tumor Segmentation in MRI Images Using Deep Learning: Overview, Challenges and Future (Minakshi Sharma, Neha Miglani)....Pages 347-383

✦ Subjects

Engineering; Computational Intelligence; Biomedical Engineering; Big Data

📜 SIMILAR VOLUMES

Deep Learning Techniques for Biomedical

📁 Deep Learning Techniques for Biomedical and Health Informatics

✍ Basant Agarwal (editor), Valentina Emilia Balas (editor), Lakhmi C. Jain (editor 📂 Library 📅 2020 🏛 Academic Pr 🌐 English

Deep Learning Techniques for Biomedical and Health Informatics provides readers with the state-of-the-art in deep learning-based methods for biomedical and health informatics. The book covers not only the best-performing methods, it also presents implementation methods. The book includes a

Deep Learning, Machine Learning and IoT

📁 Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics: Techniques and Applications (Biomedical Engineering)

✍ Sujata Dash (editor), Subhendu Kumar Pani (editor), Joel Jose P. Coelho Rodrigue 📂 Library 📅 2022 🏛 CRC Press 🌐 English

Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IOT and Machine Learning based biomedical and healt

Deep Learning, Machine Learning and IoT

📁 Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics: Techniques and Applications

✍ Sujata Dash, Subhendu Kumar Pani, Joel Jose P. Coelho Rodrigues, Babita Majhi 📂 Library 📅 2022 🏛 CRC Press 🌐 English

Handbook of Deep Learning in Biomedical

📁 Handbook of Deep Learning in Biomedical Engineering and Health Informatics

✍ E. Golden Julie (editor), Y. Harold Robinson (editor), S. M. Jaisakthi (editor) 📂 Library 📅 2021 🏛 Apple Academic Press 🌐 English

This new volume discusses state-of-the-art deep learning techniques and approaches that can be applied in biomedical systems and health informatics. Deep learning in the biomedical field is an effective method of collecting and analyzing data that can be used for the accurate diagnosis of disease

Deep Learning in Biomedical and Health I

📁 Deep Learning in Biomedical and Health Informatics: Current Applications and Possibilities (Emerging Trends in Biomedical Technologies and Health informatics)

✍ M. A. Jabbar (editor), Ajith Abraham (editor), Onur Dogan (editor), Ana Maria Ma 📂 Library 📅 2021 🏛 CRC Press 🌐 English

This book provides a proficient guide on the relationship between Artificial Intelligence (AI) and healthcare and how AI is changing all aspects of the healthcare industry. It also covers how deep learning will help in diagnosis and the prediction of disease spread. The editors present a comprehe

Deep Learning for Biomedical Data Analys

📁 Deep Learning for Biomedical Data Analysis: Techniques, Approaches, and Applications

✍ Mourad Elloumi 📂 Library 📅 2021 🏛 Springer 🌐 English

<div>This book is the first overview on Deep Learning (DL) for biomedical data analysis. It surveys the most recent techniques and approaches in this field, with both a broad coverage and enough depth to be of practical use to working professionals. This book offers enough fundamental and technical