C-C1-02: Data Extraction From Text, Step 1: Preparing Test for Machine Processing

Abstract

Background: Natural language processing (NLP) uses software to assist in the extraction of information from clinical text, a process usually performed entirely by chart abstractors. Before NLP can be applied the text in question must be prepared for machine processing. In research settings this pre- processing work often involves several successive and related tasks, requiring substantial amounts of time and attention from people representing various types of clinical, scientific and technical expertise. Appreciating the tasks and participants involved in pre-processing clinical text can make the work more manageable, efficient, and effective. Methods: The information presented here comes from case study analyses of three small-scale projects involving preparation of clinical text (pathology reports, radiology reports, and progress notes) for processing by the Cancer Text Information Extraction System. Supplementing these experiences is information from anecdotal conversations with natural language processing experts. Results: Ten separate pre-processing tasks were identified: obtaining source feeds, assessing completeness, de-duplication, universe description, cleaning and formatting, de-identification, database loading, sampling, preparation of the NLP system input feed, and quality assurance. Nine types of expertise or task participants required for preprocessing were identified: IRB representative, source-system manager, network/dbase administrator, programmer, statistician, investigator, informaticist, clinical domain expert, and manual chart abstractor. Conclusions: Pre-processing clinical text is an important phase and potentially challenging aspect of extracting information from clinical text using NLP. Because researchers require accurate information about the larger universe of documents or patients represented by the sampled and processed text, pre-processing can present numerous challenges, the solutions to which draw on many areas of expertise in a multi-step and sometimes iterative process.