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Full Description
Too often, healthcare workers are led to believe that medical informatics is a complex field that can only be mastered by teams of professional programmers. This is simply not the case. With just a few dozen simple algorithms, easily implemented with open source programming languages, you can fully utilize the medical information contained in clinical and research datasets. The common computational tasks of medical informatics are accessible to anyone willing to learn the basics.
Methods in Medical Informatics: Fundamentals of Healthcare Programming in Perl, Python, and Ruby demonstrates that biomedical professionals with fundamental programming knowledge can master any kind of data collection. Providing you with access to data, nomenclatures, and programming scripts and languages that are all free and publicly available, this book —
Describes the structure of data sources used, with instructions for downloading
Includes a clearly written explanation of each algorithm
Offers equivalent scripts in Perl, Python, and Ruby, for each algorithm
Shows how to write short, quickly learned scripts, using a minimal selection of commands
Teaches basic informatics methods for retrieving, organizing, merging, and analyzing data sources
Provides case studies that detail the kinds of questions that biomedical scientists can ask and answer with public data and an open source programming language
Requiring no more than a working knowledge of Perl, Python, or Ruby, Methods in Medical Informatics will have you writing powerful programs in just a few minutes. Within its chapters, you will find descriptions of the basic methods and implementations needed to complete many of the projects you will encounter in your biomedical career.
Contents
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.
