Big science, big data, and a big role for biomedical informatics.
Author(s): Ohno-Machado, Lucila
DOI: 10.1136/amiajnl-2012-001052
Author(s): Ohno-Machado, Lucila
DOI: 10.1136/amiajnl-2012-001052
Inadequate participant recruitment is a major problem facing clinical research. Recent studies have demonstrated that electronic health record (EHR)-based, point-of-care, clinical trial alerts (CTA) can improve participant recruitment to certain clinical research studies. Despite their promise, much remains to be learned about the use of CTAs. Our objective was to study whether repeated exposure to such alerts leads to declining user responsiveness and to characterize its extent if present to [...]
Author(s): Embi, Peter J, Leonard, Anthony C
DOI: 10.1136/amiajnl-2011-000743
The spread of case-control genome-wide association studies (GWASs) has stimulated the development of new variable selection methods and predictive models. We introduce a novel Bayesian model search algorithm, Binary Outcome Stochastic Search (BOSS), which addresses the model selection problem when the number of predictors far exceeds the number of binary responses.
Author(s): Russu, Alberto, Malovini, Alberto, Puca, Annibale A, Bellazzi, Riccardo
DOI: 10.1136/amiajnl-2011-000741
Clinical research informatics is the rapidly evolving sub-discipline within biomedical informatics that focuses on developing new informatics theories, tools, and solutions to accelerate the full translational continuum: basic research to clinical trials (T1), clinical trials to academic health center practice (T2), diffusion and implementation to community practice (T3), and 'real world' outcomes (T4). We present a conceptual model based on an informatics-enabled clinical research workflow, integration across heterogeneous data sources [...]
Author(s): Kahn, Michael G, Weng, Chunhua
DOI: 10.1136/amiajnl-2012-000968
The objective of this study is to develop an approach to evaluate the quality of terminological annotations on the value set (ie, enumerated value domain) components of the common data elements (CDEs) in the context of clinical research using both unified medical language system (UMLS) semantic types and groups.
Author(s): Jiang, Guoqian, Solbrig, Harold R, Chute, Christopher G
DOI: 10.1136/amiajnl-2011-000739
Profiling the allocation and trend of research activity is of interest to funding agencies, administrators, and researchers. However, the lack of a common classification system hinders the comprehensive and systematic profiling of research activities. This study introduces ontology-based annotation as a method to overcome this difficulty. Analyzing over a decade of funding data and publication data, the trends of disease research are profiled across topics, across institutions, and over time.
Author(s): Liu, Yi, Coulet, Adrien, LePendu, Paea, Shah, Nigam H
DOI: 10.1136/amiajnl-2011-000631
To characterise empirical instances of Unified Medical Language System (UMLS) Metathesaurus term strings in a large clinical corpus, and to illustrate what types of term characteristics are generalisable across data sources.
Author(s): Wu, Stephen T, Liu, Hongfang, Li, Dingcheng, Tao, Cui, Musen, Mark A, Chute, Christopher G, Shah, Nigam H
DOI: 10.1136/amiajnl-2011-000744
Competing tools are available online to assess the risk of developing certain conditions of interest, such as cardiovascular disease. While predictive models have been developed and validated on data from cohort studies, little attention has been paid to ensure the reliability of such predictions for individuals, which is critical for care decisions. The goal was to develop a patient-driven adaptive prediction technique to improve personalized risk estimation for clinical decision [...]
Author(s): Jiang, Xiaoqian, Boxwala, Aziz A, El-Kareh, Robert, Kim, Jihoon, Ohno-Machado, Lucila
DOI: 10.1136/amiajnl-2011-000751
Clinical integrated data repositories (IDRs) are poised to become a foundational element of biomedical and translational research by providing the coordinated data sources necessary to conduct retrospective analytic research and to identify and recruit prospective research subjects. The Clinical and Translational Science Award (CTSA) consortium's Informatics IDR Group conducted a survey of 2010 consortium members to evaluate recent trends in IDR implementation and use to support research between 2008 and [...]
Author(s): MacKenzie, Sandra L, Wyatt, Matt C, Schuff, Robert, Tenenbaum, Jessica D, Anderson, Nick
DOI: 10.1136/amiajnl-2011-000508
Electronic health records (EHR) can allow for the generation of large cohorts of individuals with given diseases for clinical and genomic research. A rate-limiting step is the development of electronic phenotype selection algorithms to find such cohorts. This study evaluated the portability of a published phenotype algorithm to identify rheumatoid arthritis (RA) patients from EHR records at three institutions with different EHR systems.
Author(s): Carroll, Robert J, Thompson, Will K, Eyler, Anne E, Mandelin, Arthur M, Cai, Tianxi, Zink, Raquel M, Pacheco, Jennifer A, Boomershine, Chad S, Lasko, Thomas A, Xu, Hua, Karlson, Elizabeth W, Perez, Raul G, Gainer, Vivian S, Murphy, Shawn N, Ruderman, Eric M, Pope, Richard M, Plenge, Robert M, Kho, Abel Ngo, Liao, Katherine P, Denny, Joshua C
DOI: 10.1136/amiajnl-2011-000583