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Data Warehousing for Biomedical Informatics is a step-by-step how-to guide for designing and building an enterprise-wide data warehouse across a biomedical or healthcare institution, using a four-iteration lifecycle and standardized design pattern. It enables you to quickly implement a fully-scalable generic data architecture that supports your org
Advances in the biomedical sciences, especially genomics, proteomics, and metabolomics, taken together with the expanding use of electronic health records, are radically changing the IT infrastructure and software applications needed to support the transfer of knowledge from bench to bedside. Pediatric Biomedical Informatics: Computer Applications in Pediatric Research describes the core resources in informatics necessary to support biomedical research programs and how these can best be integrated with hospital systems to receive clinical information that is necessary to conduct translational research.The focus is on the authors’ recent practical experiences in establishing an informatics infrastructure in a large research-intensive children’s hospital. This book is intended for translational researchers and informaticians in pediatrics, but can also serve as a guide to all institutions facing the challenges of developing and strengthening informatics support for biomedical research. The first section of the book discusses important technical challenges underlying computer-based pediatric research, while subsequent sections discuss informatics applications that support biobanking and a broad range of research programs. Pediatric Biomedical Informatics provides practical insights into the design, implementation, and utilization of informatics infrastructures to optimize care and research to benefit children. Dr. John Hutton is the Vice President and Director of Biomedical Informatics at Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA. He is also Professor of Pediatrics and Associate Dean for Information Services at the University of Cincinnati College of Medicine.
view, showing that multiple molecular pathways must be affected for cancer to develop, but with different specific proteins in each pathway mutated or differentially expressed in a given tumor (The Cancer Genome Atlas Research Network 2008; Parsons et al. 2008). Different studies demonstrated that while widespread mutations exist in cancer, not all mutations drive cancer development (Lin et al. 2007). This suggests a need to target only a deleterious subset of aberrant proteins, since any tre- ment must aim to improve health to justify its potential side effects. Treatment for cancer must become highly individualized, focusing on the specific aberrant driver proteins in an individual. This drives a need for informatics in cancer far beyond the need in other diseases. For instance, routine treatment with statins has become widespread for minimizing heart disease, with most patients responding to standard doses (Wilt et al. 2004). In contrast, standard treatment for cancer must become tailored to the molecular phenotype of an individual tumor, with each patient receiving a different combination of therapeutics aimed at the specific aberrant proteins driving the cancer. Tracking the aberrations that drive cancers, identifying biomarkers unique to each individual for molecular-level di- nosis and treatment response, monitoring adverse events and complex dosing schedules, and providing annotated molecular data for ongoing research to improve treatments comprise a major biomedical informatics need.
Beginning with a survey of fundamental concepts associated with data integration, knowledge representation, and hypothesis generation from heterogeneous data sets, Methods in Biomedical Informatics provides a practical survey of methodologies used in biological, clinical, and public health contexts. These concepts provide the foundation for more advanced topics like information retrieval, natural language processing, Bayesian modeling, and learning classifier systems. The survey of topics then concludes with an exposition of essential methods associated with engineering, personalized medicine, and linking of genomic and clinical data. Within an overall context of the scientific method, Methods in Biomedical Informatics provides a practical coverage of topics that is specifically designed for: (1) domain experts seeking an understanding of biomedical informatics approaches for addressing specific methodological needs; or (2) biomedical informaticians seeking an approachable overview of methodologies that can be used in scenarios germane to biomedical research. - Contributors represent leading biomedical informatics experts: individuals who have demonstrated effective use of biomedical informatics methodologies in the real-world, high-quality biomedical applications - Material is presented as a balance between foundational coverage of core topics in biomedical informatics with practical "in-the-trenches" scenarios. - Contains appendices that function as primers on: (1) Unix; (2) Ruby; (3) Databases; and (4) Web Services.
The official text for the internationally accredited Diploma in Health Informatics (DipHI) at http: //www.health-informatics.co comprises 17 course/chapters covering 350 topics and includes 473 regularly updated internet resource "elinks." Health Informatics is an emerging discipline. The recording and communication of information has always been essential to the practice of medicine but we live in a global, electronic age, and never before have the opportunities, the risks and the need for information governance been so crucial and at such a scale. With content from the UK, Ireland, Canada, USA, Australia and New Zealand, the DipHI covers what you need to know to take on a Master's level Health Informatics degree.
Technological infrastructure - Standards for interworking - Human-computer interaction - Knowledge representation - Information management - Decision support - Electronic patient records - Health information systems - Patient care aspects/telematics.
An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more. Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.
Preceded by: Ethical challenges in the management of health information / [edited by ] Laurinda Beebe Harman. 2nd edition. 2006.
This trailblazing resource on biomedical informatics provides medical researchers with innovative techniques for integrating and federating data from clinical and molecular studies. This volume helps researchers manage data, expedite their efforts, and make the most of targeted basic research.
This book constitutes the refereed proceedings of the International Workshop on Pattern Recognition in Bioinformatics, PRIB 2006, held in Hong Kong, within the scope of the 18th International Conference on Pattern Recognition, ICPR 2006. The book presents 19 revised full papers, covering all topics of the creation and maintenance of biological databases, and the discovery of knowledge from life sciences data. Includes an introduction to Pattern Recognition in Bioinformatics.