Consider natural language processing (NLP), a technology that can produce readable summaries of chunks of text. Basic examples of NLP include social media, newspaper articles, and, as the Parliament of Canada and the European Union have done, translating governmental proceedings into all official languages. But this is just the tip
Objective Natural language processing NLP tasks are commonly decomposed into subtasks, chained together to form processing pipelines. The residual error produced in these subtasks propagates, adversely affecting the end objectives. Limited availability of annotated clinical data remains a barrier to reaching state-of-the-art operating characteristics using statistically based NLP tools in
Objective Significant limitations exist in the timely and complete identification of primary and recurrent cancers for clinical and epidemiologic research. A SAS-based coding, extraction, and nomenclature tool (SCENT) was developed to address this problem.
Materials and methods SCENT employs hierarchical classification rules to identify and extract information from electronic pathology reports. Reports are