BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous…mehr
This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications.
Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient's data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients' biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients.
Audience
Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
Produktdetails
Artificial Intelligence and Soft Computing for Industrial Transformation
Sujata Dash received her PhD in Computational Modeling from Berhampur University, Orissa, India in 1995. She is an associate professor in P.G. Department of Computer Science & Application, North Orissa University, at Baripada, India. She has published more than 80 technical papers in international journals, conferences, book chapters and has authored 5 books. Subhendu Kumar Pani received his PhD from Utkal University Odisha, India in 2013. He is working as Professor in the Krupajal Computer Academy, BPUT, Odisha, India. S. Balamurugan is the Director-Research and Development, Intelligent Research Consultancy Services(iRCS), Coimbatore, Tamilnadu, India. His PhD is in Infomation Technology and he has published 45 books, 200+ international journals/conferences and 35 patents. Ajith Abraham received PhD in Computer Science from Monash University, Melbourne, Australia in 2001. He is Director of Machine Intelligence Research Labs (MIR Labs) which has members from 100+ countries. Ajith's research experience includes over 30 years in the industry and academia. He has authored / co-authored over 1300+ publications (with colleagues from nearly 40 countries) and has an h-index of 86+.
Inhaltsangabe
Preface xv
1 Mortality Prediction of ICU Patients Using Machine Learning Techniques 1 Babita Majhi, Aarti Kashyap and Ritanjali Majhi
1.1 Introduction 2
1.2 Review of Literature 3
1.3 Materials and Methods 8
1.3.1 Dataset 8
1.3.2 Data Pre-Processing 8
1.3.3 Normalization 8
1.3.4 Mortality Prediction 10
1.3.5 Model Description and Development 11
1.4 Result and Discussion 15
1.5 Conclusion 16
1.6 Future Work 16
References 17
2 Artificial Intelligence in Bioinformatics 21 V. Samuel Raj, Anjali Priyadarshini, Manoj Kumar Yadav, Ramendra Pati Pandey, Archana Gupta and Arpana Vibhuti
2.1 Introduction 21
2.2 Recent Trends in the Field of AI in Bioinformatics 22
2.2.1 DNA Sequencing and Gene Prediction Using Deep Learning 24
2.3 Data Management and Information Extraction 26
2.4 Gene Expression Analysis 26
2.4.1 Approaches for Analysis of Gene Expression 27
2.4.2 Applications of Gene Expression Analysis 29
2.5 Role of Computation in Protein Structure Prediction 30
2.6 Application in Protein Folding Prediction 31
2.7 Role of Artificial Intelligence in Computer-Aided Drug Design 38
2.8 Conclusions 42
References 43
3 Predictive Analysis in Healthcare Using Feature Selection 53 Aneri Acharya, Jitali Patel and Jigna Patel
3.1 Introduction 54
3.1.1 Overview and Statistics About the Disease 54
3.1.1.1 Diabetes 54
3.1.1.2 Hepatitis 55
3.1.2 Overview of the Experiment Carried Out 56
3.2 Literature Review 58
3.2.1 Summary 58
3.2.2 Comparison of Papers for Diabetes and Hepatitis Dataset 61
3.3 Dataset Description 70
3.3.1 Diabetes Dataset 70
3.3.2 Hepatitis Dataset 71
3.4 Feature Selection 73
3.4.1 Importance of Feature Selection 74
3.4.2 Difference Between Feature Selection, Feature Extraction and Dimensionality Reduction 74
3.4.3 Why Traditional Feature Selection Techniques Still Holds True? 75
3.4.4 Advantages and Disadvantages of Feature Selection Technique 76
3.4.4.1 Advantages 76
3.4.4.2 Disadvantage 76
3.5 Feature Selection Methods 76
3.5.1 Filter Method 76
3.5.1.1 Basic Filter Methods 77
3.5.1.2 Correlation Filter Methods 77
3.5.1.3 Statistical & Ranking Filter Methods 78
3.5.1.4 Advantages and Disadvantages of Filter Method 80
3.5.2 Wrapper Method 80
3.5.2.1 Advantages and Disadvantages of Wrapper Method 82
3.5.2.2 Difference Between Filter Method and Wrapper Method 82
3.6 Methodology 84
3.6.1 Steps Performed 84
3.6.2 Flowchart 84
3.7 Experimental Results and Analysis 85
3.7.1 Task 1--Application of Four Machine Learning Models 85
1 Mortality Prediction of ICU Patients Using Machine Learning Techniques 1 Babita Majhi, Aarti Kashyap and Ritanjali Majhi
1.1 Introduction 2
1.2 Review of Literature 3
1.3 Materials and Methods 8
1.3.1 Dataset 8
1.3.2 Data Pre-Processing 8
1.3.3 Normalization 8
1.3.4 Mortality Prediction 10
1.3.5 Model Description and Development 11
1.4 Result and Discussion 15
1.5 Conclusion 16
1.6 Future Work 16
References 17
2 Artificial Intelligence in Bioinformatics 21 V. Samuel Raj, Anjali Priyadarshini, Manoj Kumar Yadav, Ramendra Pati Pandey, Archana Gupta and Arpana Vibhuti
2.1 Introduction 21
2.2 Recent Trends in the Field of AI in Bioinformatics 22
2.2.1 DNA Sequencing and Gene Prediction Using Deep Learning 24
2.3 Data Management and Information Extraction 26
2.4 Gene Expression Analysis 26
2.4.1 Approaches for Analysis of Gene Expression 27
2.4.2 Applications of Gene Expression Analysis 29
2.5 Role of Computation in Protein Structure Prediction 30
2.6 Application in Protein Folding Prediction 31
2.7 Role of Artificial Intelligence in Computer-Aided Drug Design 38
2.8 Conclusions 42
References 43
3 Predictive Analysis in Healthcare Using Feature Selection 53 Aneri Acharya, Jitali Patel and Jigna Patel
3.1 Introduction 54
3.1.1 Overview and Statistics About the Disease 54
3.1.1.1 Diabetes 54
3.1.1.2 Hepatitis 55
3.1.2 Overview of the Experiment Carried Out 56
3.2 Literature Review 58
3.2.1 Summary 58
3.2.2 Comparison of Papers for Diabetes and Hepatitis Dataset 61
3.3 Dataset Description 70
3.3.1 Diabetes Dataset 70
3.3.2 Hepatitis Dataset 71
3.4 Feature Selection 73
3.4.1 Importance of Feature Selection 74
3.4.2 Difference Between Feature Selection, Feature Extraction and Dimensionality Reduction 74
3.4.3 Why Traditional Feature Selection Techniques Still Holds True? 75
3.4.4 Advantages and Disadvantages of Feature Selection Technique 76
3.4.4.1 Advantages 76
3.4.4.2 Disadvantage 76
3.5 Feature Selection Methods 76
3.5.1 Filter Method 76
3.5.1.1 Basic Filter Methods 77
3.5.1.2 Correlation Filter Methods 77
3.5.1.3 Statistical & Ranking Filter Methods 78
3.5.1.4 Advantages and Disadvantages of Filter Method 80
3.5.2 Wrapper Method 80
3.5.2.1 Advantages and Disadvantages of Wrapper Method 82
3.5.2.2 Difference Between Filter Method and Wrapper Method 82
3.6 Methodology 84
3.6.1 Steps Performed 84
3.6.2 Flowchart 84
3.7 Experimental Results and Analysis 85
3.7.1 Task 1--Application of Four Machine Learning Models 85
