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Natural Language Processing for Clinical Excellence: The State of Practices, Opportunities, and Challenges

Rapid growth in the adoption of electronic health records (EHRs) has led to an unprecedented expansion in the availability of large longitudinal datasets Large initiatives such as the Electronic Medical Records and Genomics (eMERGE) Network, the Patient-Centered Outcomes Research Network (PCORNet), and the Observational Health Data Science and Informatics (OHDSI) consortium, have been established and have reported successful applications of secondary use of EHRs in clinical research and practice.

In these applications, natural language processing (NLP) technologies have played a crucial role as much of detailed patient information in EHRs is embedded in narrative clinical documents.

Meanwhile, a number of clinical NLP systems, such as MedLEE MetaMap MetaMap Lite, cTAKES MedTagger and i2b2 have been developed and utilized to extract useful information from diverse types of clinical text, such as clinical notes, radiology reports, and pathology reports.

This talk will walk through some successful applications of NLP techniques in the clinical domain with potential opportunities and challenges.

Radiology Report Summarization

Impressions in a radiology report is a summary that represents the radiologist’s reasoning and conclusion on an imaging study, and it also...