MOTIVATION: In the post genome-wide association study (GWAS) era, omics techniques have characterized information beyond genomic variants to include cell and tissue type-specific gene transcription, transcription factor binding sites, expression quantitative trait loci (eQTL) and many other biological layers. Analysis of omics data and its integration has in turn improved the functional interpretation of disease-associated genetic variants. Over 170 000 transcriptomic and epigenomic datasets corresponding to studies of various cell and tissue types under specific disease, treatment and exposure conditions are available in the Gene Expression Omnibus resource. Although these datasets are valuable to guide the design of experimental validation studies to understand the function of disease-associated genetic loci, in their raw form, they are not helpful to experimental researchers who lack adequate computational resources or experience analyzing omics data. We sought to create an integrated re-source of tissue-specific results from omics studies that is guided by disease-specific knowledge to facilitate the design of experiments that can provide biologically meaningful insights into genetic associations. RESULTS: We designed the Reducing Associations by Linking Genes and omics Results web app to provide multi-layered omics information based on results from GWAS, transcriptomic, epigenomic and eQTL studies for gene-centric analysis and visualization. With a focus on asthma datasets, the integrated omics results it contains facilitate the formulation of hypotheses related to airways disease-associated genes and can be addressed with experimental validation studies. AVAILABILITY AND IMPLEMENTATION: The REALGAR web app is available at: http://realgar.org/. The source code is available at: https://github.com/HimesGroup/realgar. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

OBJECTIVE: Frailty is a prevalent risk factor for adverse outcomes among patients with chronic lung disease. However, identifying frail patients who may benefit from interventions is challenging using standard data sources. We therefore sought to identify phrases in clinical notes in the electronic health record (EHR) that describe actionable frailty syndromes. MATERIALS AND METHODS: We used an active learning strategy to select notes from the EHR and annotated each sentence for 4 actionable aspects of frailty: respiratory impairment, musculoskeletal problems, fall risk, and nutritional deficiencies. We compared the performance of regression, tree-based, and neural network models to predict the labels for each sentence. We evaluated performance with the scaled Brier score (SBS), where 1 is perfect and 0 is uninformative, and the positive predictive value (PPV). RESULTS: We manually annotated 155 952 sentences from 326 patients. Elastic net regression had the best performance across all 4 frailty aspects (SBS 0.52, 95% confidence interval [CI] 0.49-0.54) followed by random forests (SBS 0.49, 95% CI 0.47-0.51), and multi-task neural networks (SBS 0.39, 95% CI 0.37-0.42). For the elastic net model, the PPV for identifying the presence of respiratory impairment was 54.8% (95% CI 53.3%-56.6%) at a sensitivity of 80%. DISCUSSION: Classification models using EHR notes can effectively identify actionable aspects of frailty among patients living with chronic lung disease. Regression performed better than random forest and neural network models. CONCLUSIONS: NLP-based models offer promising support to population health management programs that seek to identify and refer community-dwelling patients with frailty for evidence-based interventions.

OBJECTIVE: Identify clinical factors that modulate the risk of progression to COPD among asthma patients using data extracted from electronic medical records. DESIGN: Demographic information and comorbidities from adult asthma patients who were observed for at least 5 years with initial observation dates between 1988 and 1998, were extracted from electronic medical records of the Partners Healthcare System using tools of the National Center for Biomedical Computing "Informatics for Integrating Biology to the Bedside" (i2b2). MEASUREMENTS: A predictive model of COPD was constructed from a set of 9,349 patients (843 cases, 8,506 controls) using Bayesian networks. The model's predictive accuracy was tested using it to predict COPD in a future independent set of asthma patients (992 patients; 46 cases, 946 controls), who had initial observation dates between 1999 and 2002. RESULTS: A Bayesian network model composed of age, sex, race, smoking history, and 8 comorbidity variables is able to predict COPD in the independent set of patients with an accuracy of 83.3%, computed as the area under the Receiver Operating Characteristic curve (AUROC). CONCLUSIONS: Our results demonstrate that data extracted from electronic medical records can be used to create predictive models. With improvements in data extraction and inclusion of more variables, such models may prove to be clinically useful.