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

Background: Natural 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. Results: We 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. Conclusion: NOBLE 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

Automated Detection of Systematic Off-label Drug Use in Free Text of Electronic Medical Records.

Off-label use of a drug occurs when it is used in a manner that deviates from its FDA label. Studies estimate that 21% of prescriptions are off-label, with only 27% of those uses supported by evidence of safety and efficacy. We have developed methods to detect population level off-label usage using computationally efficient annotation of free text from clinical notes to generate features encoding empirical information about drug-disease mentions. By including additional features encoding prior knowledge about drugs, diseases, and known usage, we trained a highly accurate predictive model that was used to detect novel candidate off-label usages in a very large clinical corpus. We show that the candidate uses are plausible and can be prioritized for further analysis in terms of safety and efficacy

Automated recognition and post-coordination of complex clinical terms

One of the key tasks in integrating guideline-based decision support systems with the electronic patient record is the mapping of clinical terms contained in both guidelines and patient notes to a common, controlled terminology. However, a vocabulary of pre-coordinated terms cannot cover every possible variation - clinical terms are often highly compositional and complex. We present a rule-based approach for automated recognition and post-coordination of clinical terms using minimal, morpheme-based thesauri, neoclassical combining forms and part-of-speech analysis. The process integrates MetaMap with the open-source GATE framework

Profiling risk factors for chronic uveitis in juvenile idiopathic arthritis: a new model for EHR-based research.

BackgroundJuvenile idiopathic arthritis is the most common rheumatic disease in children. Chronic uveitis is a common and serious comorbid condition of juvenile idiopathic arthritis, with insidious presentation and potential to cause blindness. Knowledge of clinical associations will improve risk stratification. Based on clinical observation, we hypothesized that allergic conditions are associated with chronic uveitis in juvenile idiopathic arthritis patients.MethodsThis study is a retrospective cohort study using Stanford's clinical data warehouse containing data from Lucile Packard Children's Hospital from 2000-2011 to analyze patient characteristics associated with chronic uveitis in a large juvenile idiopathic arthritis cohort. Clinical notes in patients under 16 years of age were processed via a validated text analytics pipeline. Bivariate-associated variables were used in a multivariate logistic regression adjusted for age, gender, and race. Previously reported associations were evaluated to validate our methods. The main outcome measure was presence of terms indicating allergy or allergy medications use overrepresented in juvenile idiopathic arthritis patients with chronic uveitis. Residual text features were then used in unsupervised hierarchical clustering to compare clinical text similarity between patients with and without uveitis.ResultsPreviously reported associations with uveitis in juvenile idiopathic arthritis patients (earlier age at arthritis diagnosis, oligoarticular-onset disease, antinuclear antibody status, history of psoriasis) were reproduced in our study. Use of allergy medications and terms describing allergic conditions were independently associated with chronic uveitis. The association with allergy drugs when adjusted for known associations remained significant (OR 2.54, 95% CI 1.22-5.4).ConclusionsThis study shows the potential of using a validated text analytics pipeline on clinical data warehouses to examine practice-based evidence for evaluating hypotheses formed during patient care. Our study reproduces four known associations with uveitis development in juvenile idiopathic arthritis patients, and reports a new association between allergic conditions and chronic uveitis in juvenile idiopathic arthritis patients

Comparison of automated and human assignment of MeSH terms on publicly-available molecular datasets

AbstractPublicly available molecular datasets can be used for independent verification or investigative repurposing, but depends on the presence, consistency and quality of descriptive annotations. Annotation and indexing of molecular datasets using well-defined controlled vocabularies or ontologies enables accurate and systematic data discovery, yet the majority of molecular datasets available through public data repositories lack such annotations. A number of automated annotation methods have been developed; however few systematic evaluations of the quality of annotations supplied by application of these methods have been performed using annotations from standing public data repositories. Here, we compared manually-assigned Medical Subject Heading (MeSH) annotations associated with experiments by data submitters in the PRoteomics IDEntification (PRIDE) proteomics data repository to automated MeSH annotations derived through the National Center for Biomedical Ontology Annotator and National Library of Medicine MetaMap programs. These programs were applied to free-text annotations for experiments in PRIDE. As many submitted datasets were referenced in publications, we used the manually curated MeSH annotations of those linked publications in MEDLINE as “gold standard”. Annotator and MetaMap exhibited recall performance 3-fold greater than that of the manual annotations. We connected PRIDE experiments in a network topology according to shared MeSH annotations and found 373 distinct clusters, many of which were found to be biologically coherent by network analysis. The results of this study suggest that both Annotator and MetaMap are capable of annotating public molecular datasets with a quality comparable, and often exceeding, that of the actual data submitters, highlighting a continuous need to improve and apply automated methods to molecular datasets in public data repositories to maximize their value and utility

Annotation analysis for testing drug safety signals using unstructured clinical notes

BackgroundThe electronic surveillance for adverse drug events is largely based upon the analysis of coded data from reporting systems. Yet, the vast majority of electronic health data lies embedded within the free text of clinical notes and is not gathered into centralized repositories. With the increasing access to large volumes of electronic medical data-in particular the clinical notes-it may be possible to computationally encode and to test drug safety signals in an active manner.ResultsWe describe the application of simple annotation tools on clinical text and the mining of the resulting annotations to compute the risk of getting a myocardial infarction for patients with rheumatoid arthritis that take Vioxx. Our analysis clearly reveals elevated risks for myocardial infarction in rheumatoid arthritis patients taking Vioxx (odds ratio 2.06) before 2005.ConclusionsOur results show that it is possible to apply annotation analysis methods for testing hypotheses about drug safety using electronic medical records