Search AMIA.org

We appreciate the detailed review provided by Magge et al1 of our article, "Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts." 2 In their letter, they present a subjective criticism that rests on concerns about our dataset composition and potential misinterpretation of comparisons to existing methods. Our article underwent two rounds of extensive peer review and has been cited 28 times1 in [...]

We describe the development of a nursing home information technology (IT) maturity model designed to capture stages of IT maturity.

Genetic ancestry is a critical co-factor to study phenotype-genotype associations using cohorts of human subjects. Most publicly available molecular datasets are, however, missing this information or only share self-reported race and ethnicity, representing a limitation to identify and repurpose datasets to investigate the contribution of ancestry to diseases and traits. We propose an analytical framework to enrich the metadata from publicly available cohorts with genetic ancestry information and a resulting [...]

Clinician information overload is prevalent in critical care settings. Improved visualization of patient information may help clinicians cope with information overload, increase efficiency, and improve quality. We compared the effect of information display interventions with usual care on patient care outcomes.

This study evaluated the degree to which recommendations for demographic data standardization improve patient matching accuracy using real-world datasets.

To introduce healthcare or biomedical blockchain applications and their underlying blockchain platforms, compare popular blockchain platforms using a systematic review method, and provide a reference for selection of a suitable blockchain platform given requirements and technical features that are common in healthcare and biomedical research applications.

Decentralized privacy-preserving predictive modeling enables multiple institutions to learn a more generalizable model on healthcare or genomic data by sharing the partially trained models instead of patient-level data, while avoiding risks such as single point of control. State-of-the-art blockchain-based methods remove the "server" role but can be less accurate than models that rely on a server. Therefore, we aim at developing a general model sharing framework to preserve predictive correctness [...]

Growth in big data and its potential impact on the healthcare industry have driven the need for more data scientists. In health care, big data can be used to improve care quality, increase efficiency, lower costs, and drive innovation. Given the importance of data scientists to U.S. healthcare organizations, I examine the qualifications and skills these organizations require for data scientist positions and the specific focus of their work.