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Post-Informatics pathology

2011; Elsevier BV; Volume: 2; Issue: 1 Linguagem: Inglês

10.4103/2153-3539.78499

2153-3539

Jules J. Berman,

Biomedical Text Mining and Ontologies

During 1970s and 1980s, pathology departments interfaced laboratory instruments and pathologists with computers, permitting the acquisition of large amounts of clinical pathology and anatomic pathology data in digital form. Over the next several decades, pathology data were collected and organized, while contributors from many ancillary fields (e.g., computer science, image analysis, statistics, cryptography, library science, electronic communication, ethics, and law) developed tools for exchanging and analyzing large data sets derived from many diverse sources. In 2010, we have: Nomenclatures to express medical data (e.g., MeSH, NCI Thesaurus, and LOINC) A method to describe data with metadata (XML, eXtensible Markup Language), a specification for binding XML-described data to unique objects (RDF, Resource Description Framework), and an ontology language for relating classes of information (e.g., OWL, Web Ontology Language). Electronic Medical Records (EMRs), wherein hospital information systems attach all the information collected on a patient to the patient's unique identifier, creating a well-specified database for every patient. Laws, regulations, and guidelines detailing how clinical data can be shared in a manner that protects patients from harm. Algorithms and software implementations for deidentifying and encrypting confidential medical data. Methods for finding and clustering data, by feature similarities, and for building a hierarchical grouping that interrelates the clusters. Other methods find trends in data, or find the best cut-off points that distinguish one class of data from another. General cross-platform scripting languages, such as Perl, Python, and Ruby, that provide nonprogrammers with the tools to write their own implementations of fundamental data analysis algorithms or to call, from their own scripts, any of thousands of publicly available method modules. Specialized scripting languages that support specific types of tasks (e.g., R for statistics, ImageMagick for imaging, POV-Ray for 3-D visualizations, and Tcl/Tk for graphic user interfaces). Standard protocols for sharing data and data services across networks (e.g., web services, cloud computing). All the listed tools are available at no cost, as either royalty-free, open source, or public domain products. Moreover, there is a rich literature, in journals, in books, and on the web, that explains how these resources can be obtained and used. The acquisition of immense data resources and of the tools to analyze the data marks the arrival of the post-informatics age. Pathologists can now focus their efforts on post-informatics questions. Here are just a few.