Search AMIA.org

Biomedical named entity recognition (BNER) is a critical component in automated systems that mine biomedical knowledge in free text. Among different types of entities in the domain, gene/protein would be the most studied one for BNER. Our goal is to develop a gene/protein name recognition system BioTagger-GM that exploits rich information in terminology sources using powerful machine learning frameworks and system combination.

While our Y2K worries about old computers "retiring" at midnight captured the television and news media attention, a more significant "old age" phenomenon snuck onto the scene with hardly a headline: the dawn of the age of the aged. (1) The over burdened health care system will face a worldwide wave of retirees who will live longer, cost more to treat, and demand new goods and services to help them [...]

To measure the uncertainty of temporal assertions like "3 weeks ago" in clinical texts.

The Program Requirements for Fellowship Education identify the knowledge and skills that physicians must master through the course of a training program to be certified in the subspecialty of clinical informatics. They also specify accreditation requirements for clinical informatics training programs. The AMIA Board of Directors approved this document in November 2008.

As the professional home for biomedical and health informaticians, AMIA is actively working to support high quality relevant professional education and research opportunities. This issue of JAMIA presents two key documents that provide tangible evidence of progress on this front. In this editorial, we describe the context and specific purpose of the two documents, how they were developed, and AMIA's plans to build upon the documents.

Advances in high-throughput and mass-storage technologies have led to an information explosion in both biology and medicine, presenting novel challenges for analysis and modeling. With regards to multivariate analysis techniques such as clustering, classification, and regression, large datasets present unique and often misunderstood challenges. The authors' goal is to provide a discussion of the salient problems encountered in the analysis of large datasets as they relate to modeling and inference [...]

To predict student performance in an introductory graduate-level biomedical informatics course from application data.

Computational support of clinical decisions frequently requires the integration of data in a variety of formats and from multiple sources and domains. Some impressive multiscale computational models of biological phenomena have been developed as part of the study of disease and healthcare systems. One can now contemplate harnessing these models arising from computational biology and using highly interconnected clinical data to support clinical decision-making. Indeed, understanding how to build computational [...]

This study evaluated the performance of an electronic screening (E-screening) method and used it to recruit patients for the NIH sponsored ACCORD trial. Out of the 193 E-screened patients, 125 met the age criterion ("age>or=40"). For all of these 125 patients, the performance of E-screening was compared with investigator review. E-screening achieved a negative predictive accuracy of 100% (95% CI: 98-100%), a positive predictive accuracy of 13% (95% CI: 6-13%) [...]

The Department of Veterans Affairs (VA) has a system-wide, patient-centric electronic medical record system (EMR) within which the authors developed the Clinical Case Registries (CCR) to support population-centric delivery and evaluation of VA medical care. To date, the authors have applied the CCR to populations with human immunodeficiency virus (HIV) and hepatitis C virus (HCV). Local components use diagnosis codes and laboratory test results to identify patients who may have [...]