ch. 1. Parsing and transforming text files -- ch. 2. Utility scripts -- ch. 3. Viewing and modifying images -- ch. 4. Indexing text -- ch. 5. The National Library of Medicine's Medical subject headings (MeSH) -- ch. 7. SEER -- ch. 8. OMIM -- ch. 9. PubMed -- ch. 10. Taxonomy -- ch. 11. Developmental lineage classification and taxonomy of neoplasms -- ch. 12. U.S. Census files -- ch. 13. Centers for Disease Control and Prevention mortality files -- ch. 14. Autocoding -- ch. 15. Text scrubber for deidentifying confidential text -- ch. 16. Web pages and CGI scripts -- ch. 17. Image annotation -- ch. 18. Describing data with data using XML -- ch. 19-27. Case stud[ies] -- Epilogue for healthcare professionals and medical scientists
Summary
A user friendly, step-by-step practical guide for applying basic informatics algorithms to medical data sets, this book includes examples using three most common programming languages (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 provides basic methods for retrieving, organizing, merging, and analyzing their data sources. It cov
