Biomedical Data Mining for Information Retrieval
Methodologies, Techniques, and Applications
- English
- PDF
- Available on iOS & Android
Biomedical Data Mining for Information Retrieval
Methodologies, Techniques, and Applications
About this book
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 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.
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Information
Table of contents
- Cover
- Half-Title Page
- Series Page
- Title Page
- Copyright Page
- Contents
- Preface
- 1 Mortality Prediction of ICU Patients Using Machine Learning Techniques
- 2 Artificial Intelligence in Bioinformatics
- 3 Predictive Analysis in Healthcare Using Feature Selection
- 4 Healthcare 4.0: An Insight of Architecture, Security Requirements, Pillars and Applications
- 5 Improved Social Media Data Mining for Analyzing Medical Trends
- 6 Bioinformatics: An Important Tool in Oncology
- 7 Biomedical Big Data Analytics Using IoT in Health Informatics
- 8 Statistical Image Analysis of Drying Bovine Serum Albumin Droplets in Phosphate Buffered Saline
- 9 Introduction to Deep Learning in Health Informatics
- 10 Data Mining Techniques and Algorithms in Psychiatric Health: A Systematic Review
- 11 Deep Learning Applications in Medical Image Analysis
- 12 Role of Medical Image Analysis in Oncology
- 13 A Comparative Analysis of Classifiers Using Particle Swarm Optimization-Based Feature Selection
- Index
- EULA