Biomedical Data Mining for Information Retrieval. Группа авторов

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      Preface

      Introduction

      Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval, which is an emerging research field at the intersection of information science and computer science. Biomedical and health informatics is another remerging field of research at the intersection of information science, computer science and healthcare. This new era of healthcare informatics and analytics brings with it tremendous opportunities and challenges based on the abundance of biomedical data easily available for further analysis. The aim of healthcare informatics is to ensure high-quality, efficient healthcare and better treatment and quality of life by efficiently analyzing biomedical and healthcare data, including patients’ data, electronic health records (EHRs) and lifestyle. Earlier, it was commonly required to have a domain expert develop a model for biomedical or healthcare data; however, recent advancements in representation learning algorithms allow automatic learning of the pattern and representation of given data for the development of such a model. Biomedical image mining is a novel research area brought about by the large number of biomedical images increasingly being generated and stored digitally. These images are mainly generated by 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 critical questions related to their healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can aid doctors in treating their patients.

Information retrieval (IR) methods have multiple levels of representation in which the system learns raw to higher abstract level representation at each level. An essential issue in medical IR is the variety of users of different services. In general, they will have changeable categories of information needs, varying levels of medical knowledge and varying language skills. The various categories of users of medical IR systems have multiple levels of medical knowledge, with the medical knowledge of many individuals falling within a category that varies greatly. This influences the way in which individuals present search queries to systems and also the level of complexity of information that should be returned to them or the type of support when considering which retrieved material should be provided. These have shown significant success in dealing with massive data for a large number of applications due to their capability of extracting complex hidden features and learning efficient representation in an unsupervised setting.

      This book covers the latest advances and developments in health informatics, data mining, machine learning and artificial intelligence, fields which to a great extent will play a vital role in improving human life. It also covers the IR-based models for biomedical and health informatics which have recently emerged in the still-developing field of research in biomedicine and healthcare. All researchers and practitioners working in the fields of biomedicine, health informatics, and information retrieval will find the book highly beneficial. Since it is a good collection of state-of-the-art approaches for data-mining-based biomedical and health-related applications, it will also be very beneficial for new researchers and practitioners working in the field in order to quickly know what the best performing methods are. With this book they will be able to compare different approaches in order to carry forward their research in the most important areas of research, which directly impacts the betterment of human life and health. No other book on the market provides such a good collection of state-of-the-art methods for mining biomedical text, images and visual features towards information retrieval.

Organization of the Book

      The 13 chapters of this book present scientific concepts, frameworks and ideas on biomedical data analytics and information retrieval from the different biomedical domains. The Editorial Advisory Board and expert reviewers have ensured the high caliber of the chapters through careful refereeing of the submitted papers. For the purpose of coherence, we have organized the chapters with respect to similarity of topics addressed, ranging from issues pertaining to the internet of things for biomedical engineering and health informatics, computational intelligence for medical image processing, and biomedical natural language processing.

In Chapter 1, “Mortality Prediction of ICU Patients Using Machine Learning Techniques,” Babita Majhi, Aarti Kashyap and Ritanjali Majhi present a mortality prediction using machine learning techniques. Since the intensive care unit (ICU) admits very ill patients, facilitating their care requires serious attention and treatment using ventilators and other sophisticated medical equipment. This equipment is very costly; hence, its optimized use is necessary. ICUs require a higher number of staff in comparison to the number of patients admitted for regular monitoring. In brief, ICUs involve a larger budget compared to other sections of any hospital. Therefore, to help doctors determine which patient is more at risk, mortality prediction is an important area of research. In data mining, mortality prediction is a binary classification problem, i.e., die or survive. As a result, this has attracted machine learning groups to apply algorithms to do the mortality prediction. In this chapter, six different machine learning methods, functional link artificial neural network (FLANN), support vector machine (SVM), discriminate analysis (DA), decision tree (DT), naïve Bayesian network and K-nearest neighbors (KNN), are used to develop a model for mortality prediction collecting data from PhysioNetChallenge 2012 and did the performance analysis of them.

      In Chapter 2, “Artificial Intelligence in Bioinformatics,” V. Samuel Raj, Anjali Priyadarshini, Manoj Kumar Yadav, Ramendra Pati Pandey, Archana Gupta and Arpana Vibhuti emphasize the various smart tools available in the field of biomedical and health informatics. They also analyzed recently introduced state-of-the-art bioinformatics using complex AI algorithms.

      In Chapter 3, “Predictive Analysis in Healthcare Using Feature Selection,” Aneri Acharya, Jitali Patel and Jigna Patel describe various methods to enhance the performance of machine learning models used in predictive analysis. The chronic diseases of diabetes and hepatitis are explored in this chapter with an experiment carried out in four tasks.

      In Chapter 4, “Healthcare 4.0: An Insight of Architecture, Security Requirements, Pillars and Applications,” Deepanshu Bajaj, Bharat Bhushan and Divya Yadav present the idea of Industry 4.0, which is massively evolving as it is essential for the medical sector, including the internet of things (IoT), big data (BD) and blockchain (BC), the combination of which are modernizing the overall framework of e-health. They analyze the implementation of the I4.0 (Industry 4.0) technology in the medical sector, which has revolutionized the best available approaches and improved the entire framework.

In Chapter 5, “Improved Social Media Data Mining for Analyzing Medical Trends,” Minakshi Sharma and Sunil Sharma discuss social media health records. Nowadays, social media has become a prominent method of sharing and viewing news among the general population. It has become an inseparable part of our lives, with people spending most of their СКАЧАТЬ