Mortality Prediction of ICU Patients Using Machine Learning Techniques / Babita Majhi, Aarti Kashyap, Ritanjali Majhi -- Artificial Intelligence in Bioinformatics / VSamuel Raj, Anjali Priyadarshini, Manoj Kumar Yadav, Ramendra Pati Pandey, Archana Gupta, Arpana Vibhuti -- Predictive Analysis in Healthcare Using Feature Selection / Aneri Acharya, Jitali Patel, Jigna Patel -- Healthcare 4.0: An Insight of Architecture, Security Requirements, Pillars and Applications / Deepanshu Bajaj, Bharat Bhushan, Divya Yadav -- Improved Social Media Data Mining for Analyzing Medical Trends / Minakshi Sharma, Sunil Sharma -- Bioinformatics: An Important Tool in Oncology / Gaganpreet Kaur, Saurabh Gupta, Gagandeep Kaur, Manju Verma, Pawandeep Kaur -- Biomedical Big Data Analytics Using IoT in Health Informatics / Pawan Singh Gangwar, Yasha Hasija -- Statistical Image Analysis of Drying Bovine Serum Albumin Droplets in Phosphate Buffered Saline / Anusuya Pal, Amalesh Gope, Germano S Iannacchione -- Introduction to Deep Learning in Health Informatics / Monika Jyotiyana, Nishtha Kesswani -- Data Mining Techniques and Algorithms in Psychiatric Health: A Systematic Review / Shikha Gupta, Nitish Mehndiratta, Swarnim Sinha, Sangana Chaturvedi, Mehak Singla -- Deep Learning Applications in Medical Image Analysis / Ananya Singha, Rini Smita Thakur, Tushar Patel -- Role of Medical Image Analysis in Oncology / Kaur Gaganpreet, Garg Hardik, Kumari Heena, Lakhvir Singh, Navroz Kaur, Shubham Kumar, Shadab Alam -- A Comparative Analysis of Classifiers Using Particle Swarm Optimization-Based Feature Selection / Chandra Sekhar Biswal, Subhendu Kumar Pani, Sujata Dash.

Includes bibliographical references and index.

Print version record.

This book 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 health care 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 its large amount of 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 related to health care. 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.

John Wiley and Sons Wiley Online Library: Complete oBooks