NOBLE – Flexible concept recognition for large-scale biomedical natural language processing

Abstract

BackgroundNatural language processing (NLP) applications are increasingly important in biomedical data analysis, knowledge engineering, and decision support. Concept recognition is an important component task for NLP pipelines, and can be either general-purpose or domain-specific. We describe a novel, flexible, and general-purpose concept recognition component for NLP pipelines, and compare its speed and accuracy against five commonly used alternatives on both a biological and clinical corpus.NOBLE Coder implements a general algorithm for matching terms to concepts from an arbitrary vocabulary set. The system’s matching options can be configured individually or in combination to yield specific system behavior for a variety of NLP tasks. The software is open source, freely available, and easily integrated into UIMA or GATE. We benchmarked speed and accuracy of the system against the CRAFT and ShARe corpora as reference standards and compared it to MMTx, MGrep, Concept Mapper, cTAKES Dictionary Lookup Annotator, and cTAKES Fast Dictionary Lookup Annotator.ResultsWe describe key advantages of the NOBLE Coder system and associated tools, including its greedy algorithm, configurable matching strategies, and multiple terminology input formats. These features provide unique functionality when compared with existing alternatives, including state-of-the-art systems. On two benchmarking tasks, NOBLE’s performance exceeded commonly used alternatives, performing almost as well as the most advanced systems. Error analysis revealed differences in error profiles among systems.ConclusionNOBLE Coder is comparable to other widely used concept recognition systems in terms of accuracy and speed. Advantages of NOBLE Coder include its interactive terminology builder tool, ease of configuration, and adaptability to various domains and tasks. NOBLE provides a term-to-concept matching system suitable for general concept recognition in biomedical NLP pipelines.

National Public Health Data Systems in the United States: Applications to Child Agricultural Injury Surveillance

“…A system that includes both coded fields and a structured narrative has much broader potential . This is especially true given recent developments in electronic text mining and machine learning that have reduced the need for manual review when using data from narrative text to help address questions in medical research …”

“…17 This is especially true given recent developments in electronic text mining and machine learning that have reduced the need for manual review when using data from narrative text to help address questions in medical research. [18][19][20][21] One exemplary North American model of an agricultural injury surveillance system that employs such an approach is the Canadian Agricultural Injury Reporting (CAIR), 22 formerly the Canadian Agricultural Injury Surveillance Program. In this program, trained coders in each participating province abstract information from coroners' and medical examiners' files (deaths) and hospital discharge records (hospitalized cases) in a standardized manner.…”

Development of an Inflammatory Bowel Disease Research Registry Derived from Observational Electronic Health Record Data for Comprehensive Clinical Phenotyping

“…For example, when extracting presence or absence of Clostridium difficile from pathology reports, 16 different spellings for “ Clostridium difficile ” were found. In order to address these issues, we began to use NOBLE tools developed at the University of Pittsburgh, Department of Biomedical Informatics, to create dictionaries and ontologies for terms of interest [16]. Furthermore, NOBLE tools have built-in NLP support for identifying context negation, helping resolve issues with false-positive term identification.…”

Natural language processing to extract symptoms of severe mental illness from clinical text: the Clinical Record Interactive Search Comprehensive Data Extraction (CRIS-CODE) project

“…Medical entities frequently targeted include medications, diagnoses, smoking status and other factors influencing risk, course or outcome for disorders of interest 2122 A large number of tools and frameworks exist for general purpose information extraction from clinical dictionaries, such as cTAKES,22 NOBLE23 and MedLee 24. However, there has been little application of NLP techniques in mental healthcare data despite the volumes of text-based information contained here, and even less on ascertaining symptomatology.…”