A user friendly, step-by-step practical guide for applying basic informatics algorithms to medical data sets, this book includes examples using Perl, Python, and Ruby. The author employs a minimal selection of commands for quick learning, and references only free, publicly available resources. This book demonstrates how easy it is to master the wide variety of data types encountered in a healthcare setting with just a few simple programming commands. It covers building blocks, medical data resources, primary tasks of medical informatics, and medical discovery.
A user friendly, step-by-step practical guide for applying basic informatics algorithms to medical data sets, this book includes examples using Perl, Python, and Ruby. The author employs a minimal selection of commands for quick learning, and references only free, publicly available resources. This book demonstrates how easy it is to master the wide variety of data types encountered in a healthcare setting with just a few simple programming commands. It covers building blocks, medical data resources, primary tasks of medical informatics, and medical discovery.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Jules Berman, Ph.D., M.D., received two bachelor of science degrees (mathematics and earth sciences) from MIT, a Ph.D. in pathology from Temple University, and an M.D. from the University of Miami School of Medicine. His postdoctoral research was conducted at the National Cancer Institute. His medical residence in pathology was completed at the George Washington University School of Medicine. He became board certified in anatomic pathology and in cytopathology, and served as the chief of Anatomic Pathology, Surgical Pathology and Cytopathology at the Veterans Administration (VA) Medical Center in Baltimore, Maryland. While at the Baltimore VA, Dr. Berman held appointments at the University of Maryland Medical Center and at theJohns Hopkins Medical Institutions. In 1998, he became the program director for pathology informatics in the Cancer Diagnosis Program at the U.S. National Cancer Institute. In 2006, he became president of the Association for Pathology Informatics. Over the course of his career, he has written, as first author, more than 100 publications, including five books in the field of medical informatics. Today, Dr. Berman is a full-time freelance writer.
Inhaltsangabe
FUNDAMENTAL ALGORITHMS AND METHODS OF MEDICAL INFORMATICS: Parsing and Transforming Text Files. Utility Scripts. Viewing and Modifying Images. MEDICAL DATA RESOURCES: The National Library of Medicine's Medical Subject Headings (MeSH ). The International Classification of Diseases. SEER: The Cancer Surveillance, Epidemiology, and End Results Program. OMIM: The Online Mendelian Inheritance in Man. PubMed. Taxonomy. Developmental Lineage Classification and Taxonomyof Neoplasms. U.S. Census Files. Centers for Disease Control and Prevention Mortality Files. PRIMARY TASKS OF MEDICAL INFORMATICS: Autocoding. Text Scrubber for Deidentifyin g Confidential Text. Web Pages and CGI Scripts. Image Annotation. Describing Data with Data, Using XML. MEDICAL DISCOVERY: Case Study: Emphysema Rates. Case Study: Cancer Occurrence Rates. Case Study: Germ Cell Tumor Rates across Ethnicities. Case Study: Ranking the Death-Certifying Process, by State. Case Study: Data Mashups for Epidemics. Case Study: Sickle Cell Rates. Case Study: Site-Specific Tumor Biology. Case Study: Bimodal Tumors. Case Study: The Age of Occurrence of Precancers. . Appendix. How to Acquire Ruby. How to Acquire Perl. How to Acquire Python. How to Acquire RMagick. How to Acquire SQLite. How to Acquire the Public Data Files Used in This Book. Other Publicly Available Files, Data Sets, and Utilities.
FUNDAMENTAL ALGORITHMS AND METHODS OF MEDICAL INFORMATICS: Parsing and Transforming Text Files. Utility Scripts. Viewing and Modifying Images. MEDICAL DATA RESOURCES: The National Library of Medicine's Medical Subject Headings (MeSH ). The International Classification of Diseases. SEER: The Cancer Surveillance, Epidemiology, and End Results Program. OMIM: The Online Mendelian Inheritance in Man. PubMed. Taxonomy. Developmental Lineage Classification and Taxonomyof Neoplasms. U.S. Census Files. Centers for Disease Control and Prevention Mortality Files. PRIMARY TASKS OF MEDICAL INFORMATICS: Autocoding. Text Scrubber for Deidentifyin g Confidential Text. Web Pages and CGI Scripts. Image Annotation. Describing Data with Data, Using XML. MEDICAL DISCOVERY: Case Study: Emphysema Rates. Case Study: Cancer Occurrence Rates. Case Study: Germ Cell Tumor Rates across Ethnicities. Case Study: Ranking the Death-Certifying Process, by State. Case Study: Data Mashups for Epidemics. Case Study: Sickle Cell Rates. Case Study: Site-Specific Tumor Biology. Case Study: Bimodal Tumors. Case Study: The Age of Occurrence of Precancers. . Appendix. How to Acquire Ruby. How to Acquire Perl. How to Acquire Python. How to Acquire RMagick. How to Acquire SQLite. How to Acquire the Public Data Files Used in This Book. Other Publicly Available Files, Data Sets, and Utilities.
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