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Large database research in axial spondyloarthritis (SpA) is limited by a lack of methods for identifying most types of axial SpA. Our objective was to develop methods for identifying axial SpA concepts in the free text of documents from electronic medical records.Veterans with documents in the national Veterans Health Administration Corporate Data Warehouse between January 1, 2005 and June 30, 2015 were included. Methods were developed for exploring, selecting, and extracting meaningful terms that were likely to represent axial SpA concepts. With annotation, clinical experts reviewed sections of text containing the meaningful terms (snippets) and classified the snippets according to whether or not they represented the intended axial SpA concept. With natural language processing (NLP) tools, computers were trained to replicate the clinical experts' snippet classifications.Three axial SpA concepts were selected by clinical experts, including sacroiliitis, terms including the prefix spond*, and HLA-B27 positivity (HLA-B27+). With supervised machine learning on annotated snippets, NLP models were developed with accuracies of 91.1% for sacroiliitis, 93.5% for spond*, and 97.2% for HLA-B27+. With independent validation, the accuracies were 92.0% for sacroiliitis, 91.0% for spond*, and 99.0% for HLA-B27+.We developed feasible and accurate methods for identifying axial SpA concepts in the free text of clinical notes. Additional research is required to determine combinations of concepts that will accurately identify axial SpA phenotypes. These novel methods will facilitate previously impractical observational research in axial SpA and may be applied to research with other diseases.

Genomic alterations of the epidermal growth factor receptor (EGFR) gene play a crucial role in pathogenesis of glioblastoma multiforme (GBM). By systematic analysis of GBM genomic data, we have identified and characterized a novel exon 27 deletion mutation occurring within the EGFR carboxyl-terminus domain (CTD), in addition to identifying additional examples of previously reported deletion mutations in this region. We show that the GBM-derived EGFR CTD deletion mutants are able to induce cellular transformation in vitro and in vivo in the absence of ligand and receptor autophosphorylation. Treatment with the EGFR-targeted monoclonal antibody, cetuximab, or the small molecule EGFR inhibitor, erlotinib, effectively impaired tumorigenicity of oncogenic EGFR CTD deletion mutants. Cetuximab in particular prolonged the survival of intracranially xenografted mice with oncogenic EGFR CTD deletion mutants, compared with untreated control mice. Therefore, we propose that erlotinib and, especially, cetuximab treatment may be a promising therapeutic strategy in GBM patients harboring EGFR CTD deletion mutants. Cancer Res; 71(24); 7587–96. ©2011 AACR.

Laypersons (''consumers'') often have difficulty finding, understanding, and acting on health in- formation due to gaps in their domain knowledge. Ideally, consumer health vocabularies (CHVs) would reflect the different ways consumers express and think about health topics, helping to bridge this vocabulary gap. However, despite the recent research on mismatches between consumer and professional language (e.g., lexical, semantic, and explanatory), there have been few systematic efforts to develop and evaluate CHVs. This paper presents the point of view that CHV development is practical and necessary for extending research on informatics-based tools to facilitate consumer health information seeking, retrieval, and understanding. In support of the view, we briefly describe a dis- tributed, bottom-up approach for (1) exploring the relationship between common consumer health expressions and professional concepts and (2) developing an open-access, preliminary (draft) ''first-generation'' CHV. While recognizing the limitations of the approach (e.g., not addressing psychosocial and cultural factors), we suggest that such explora- tory research and development will yield insights into the nature of consumer health expressions and assist developers in creating tools and applications to support consumer health information seeking. j J Am Med Inform Assoc. 2006;13:24-29. DOI 10.1197/jamia.M1761.

InterMed is a collaboration among research groups from Stanford, Harvard, and Columbia Universities. The primary goal of InterMed has been to develop a sharable language that could serve as a standard for modeling computer-interpretable guidelines (CIGs). This language, called GuideLine Interchange Format (GLIF), has been developed in a collaborative manner and in an open process that has welcomed input from the larger community. The goals and experiences of the InterMed project and lessons that the authors have learned may contribute to the work of other researchers who are developing medical knowledge-based tools. The lessons described include (1) a work process for multi-institutional research and development that considers different viewpoints, (2) an evolutionary lifecycle process for developing medical knowledge representation formats, (3) the role of cognitive methodology to evaluate and assist in the evolutionary development process, (4) development of an architecture and (5) design principles for sharable medical knowledge representation formats, and (6) a process for standardization of a CIG modeling language.

Objective: Clinical information systems typically present patient data in chronologic order, organized by the source of the information (e.g., laboratory, radiology). This study evaluates the functionality and utility of a knowledge-based system that generates concept- oriented views (organized around clinical concepts such as disease or organ system) of clinical data. Design: The authors have developed a system that uses a knowledge base of interrelationships between medical concepts to infer relationships between data in electronic medical records. They use these inferences to produce summaries, or views, of the data that are relevant to a specific concept of interest. They evaluated the ability of the system to select relevant information, reduce information overload, and support physician information retrieval. Measurements: The sensitivity and specificity of the system for identifying relevant patient information were calculated. Effect on information overload was assessed by comparing the amount of information in each view with the amount of information in the entire record. Information retrieval accuracy and cost (time) were used to measure the effect of using concept- oriented views on the efficiency and effectiveness of retrievals. Results: The sensitivity and specificity of the system for identifying relevant clinical information were generally in the range of 70 to 80 percent. Concept-oriented views are effective in reducing the amount of information retrieved (over 80 percent reduction) and, compared with source- oriented views, are able to improve physician retrieval accuracy (p = 0.04). Conclusion: Computer-generated, concept-oriented views can be used to reduce clinician information overload and improve the accuracy of clinical data retrieval.

Background: Among older adults

Monoclonal antibodies are an important resource for defining molecular expression and probing molecular function. The characterization of monoclonal antibody reactivity patterns, however, can be costly and inefficient in nonhuman experimental systems. To develop a computational approach to the pattern analysis of monoclonal antibody reactivity, we analyzed a panel of 128 monoclonal antibodies recognizing sheep antigens. Quantitative single parameter flow cytometry histograms were obtained from five cell types isolated from normal animals. The resulting 640 histograms were smoothed using a Gaussian kernel over a range of bandwidths. Histogram features were selected by SiZer—an analytic tool that identifies statistically significant features. The extracted histogram features were compared and grouped using hierarchical clustering. The validity of the clustering was indicated by the accurate pairing of externally verified molecular reactivity. We conclude that our computational algorithm is a potentially useful tool for both monoclonal antibody classification and molecular taxonomy in nonhuman experimental systems.

Background The development of consumer health information applications such as health education websites has motivated the research on consumer health vocabulary (CHV). Term identification is a critical task in vocabulary development. Because of the heterogeneity and ambiguity of consumer expressions, term identification for CHV is more challenging than for professional health vocabularies. Objective For the development of a CHV, we explored several term identification methods, including collaborative human review and automated term recognition methods. Methods A set of criteria was established to ensure consistency in the collaborative review, which analyzed 1893 strings. Using the results from the human review, we tested two automated methods—C-value formula and a logistic regression model. Results The study identified 753 consumer terms and found the logistic regression model to be highly effective for CHV term identification (area under the receiver operating characteristic curve = 95.5%). Conclusions The collaborative human review and logistic regression methods were effective for identifying terms for CHV development.

Background The text descriptions in electronic medical records are a rich source of information. We have developed a Health Information Text Extraction (HITEx) tool and used it to extract key findings for a research study on airways disease. Methods The principal diagnosis, co-morbidity and smoking status extracted by HITEx from a set of 150 discharge summaries were compared to an expert-generated gold standard. Results The accuracy of HITEx was 82% for principal diagnosis, 87% for co-morbidity, and 90% for smoking status extraction, when cases labeled "Insufficient Data" by the gold standard were excluded. Conclusion We consider the results promising, given the complexity of the discharge summaries and the extraction tasks.