Biomedical Informatics in the “Big Data” era

Health informatics (also called healthcare informatics, medical informatics, nursing informatics, clinical informatics, or biomedical informatics) is informatics in health care. It is a multidisciplinary field that uses health information technology (HIT) to improve health care via any combination of higher quality, higher efficiency and new opportunities. The disciplines involved include information science, computer science, social science, behavioral science, management science, and others. The National Library of Medicine defines health informatics as “the interdisciplinary study of the design, development, adoption and application of IT-based innovations in healthcare services delivery, management and planning.” It deals with the resources, devices, and methods required to optimize the acquisition, storage, retrieval, and use of information in health and biomedicine. It is applied to the areas of nursing, clinical care, dentistry, pharmacy, public health, occupational therapy, physical therapy and (bio)medical research, and alternative medicine, all of which are designed to improve the overall of effectiveness of patient care delivery by ensuring that the data generated is of a high quality.


In the present book, twelve typical literatures about biomedical Informatics published on international authoritative journals were selected to introduce the worldwide newest progress, which contains reviews or original researches on medical science concerning biomedical informatics. We hope this book can demonstrate advances in biomedical informatics as well as give references to the researchers, students and other related people.

Components of the Book:
  • Chapter 1
    A Review of Data Mining Using Big Data in Health Informatics
  • Chapter 2
    The Golden Era of Biomedical Informatics Has Begun
  • Chapter 3
    Next Generation Informatics for Big Data in Precision Medicine Era
  • Chapter 4
    Lactococcus Garvieae: A Small Bacteria and a Big Data World
  • Chapter 5
    Bio and Health Informatics Meets Cloud: BioVLab as an Example
  • Chapter 6
    Applications of the MapReduce Programming Framework to Clinical Big Data Analysis: Current Landscape and Future Trends
  • Chapter 7
    Computational Framework to Support Integration of Biomolecular and Clinical Data within a Translational Approach
  • Chapter 8
    Designing a Parallel Evolutionary Algorithm for Inferring Gene Networks on the Cloud Computing Environment
  • Chapter 9
    Bio-Objectifying European Bodies: Standardisation of Biobanks in the Biobanking and Biomolecular Resources Research Infrastructure
  • Chapter 10
    Integrated Bio-Search: Challenges and Trends for the Integration, Search and Comprehensive Processing of Biological Information
  • Chapter 11
    Research Prioritization through Prediction of Future Impact on Biomedical Science: A Position Paper on Inference-Analytics
  • Chapter 12
    PDON: Parkinson’s Disease Ontology for Representation and Modeling of the Parkinson’s Disease Knowledge Domain
Readership: Students, academics, teachers and other people attending or interested in Biomedical Informatics in the “Big Data” era
Jason H. Moore
Director, Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA

Yuji Zhang
PhD, Assistant Professor, Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, USA

Guillermo López-Campos
PhD, Health and Biomedical Informatics Centre (HABIC), University of Melbourne, Melbourne, Australia

Inuk Jung
PhD, Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea

Emad A. Mohammed
PhD, Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada

Marco Masseroli
PhD, Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy

and more...
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