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The utility of healthcare utilization data from US emergency departments (EDs) for rapid monitoring of changes in influenza-like illness (ILI) activity was highlighted during the recent influenza A (H1N1) pandemic. Monitoring has tended to rely on detection algorithms, such as the Early Aberration Reporting System (EARS), which are limited in their ability to detect subtle changes and identify disease trends.

Applying multiprofessional electronic health records (EHRs) is expected to improve the quality of patient care and patient safety. Both EHR systems and system users depend on semantic interoperability to function efficiently. A shared clinical terminology comprising unambiguous terms is required for semantic interoperability. Empirical studies of clinical terminology, such as predefined headings, in EHR systems are scarce and limited to one profession or one clinical specialty.

In parts of the developing world traditionally modeled healthcare systems do not adequately meet the needs of the populace. This can be due to imbalances in both supply and demand--there may be a lack of sufficient healthcare and the population most at need may be unable or unwilling to take advantage of it. Home-based care has emerged as a possible mechanism to bring healthcare to the populace in a cost-effective [...]

Although trait-associated genes identified as complex versus single-gene inheritance differ substantially in odds ratio, the authors nonetheless posit that their mechanistic concordance can reveal fundamental properties of the genetic architecture, allowing the automated interpretation of unique polymorphisms within a personal genome.

We demonstrate a genome-wide method for the integration of many studies of gene expression of phenotypically similar disease processes, a method of multiplex meta-analysis. We use immune dysfunction as an example disease process.

We describe a novel, crowdsourcing method for generating a knowledge base of problem-medication pairs that takes advantage of manually asserted links between medications and problems.

Patient discharge summaries provide detailed medical information about hospitalized patients and are a rich resource of data for clinical record text mining. The textual expressions of this information are highly variable. In order to acquire a precise understanding of the patient, it is important to uncover the relationship between all instances in the text. In natural language processing (NLP), this task falls under the category of coreference resolution.

Pharmacogenomics evaluations of variability in drug metabolic processes may be useful for making individual drug response predictions. We present an approach to deriving 'phenotype scores' based on existing pharmacogenomics knowledge and a patient's genomics data. Pharmacogenomics plays an important role in the bioactivation of tamoxifen, a prodrug administered to patients for breast cancer treatment. Tamoxifen is therefore considered a model for many drugs requiring bioactivation. We investigate whether this knowledge-based [...]