Search AMIA.org

To pilot test an infographic-based health communication intervention that our team rigorously designed and explore whether its implementation leads to better health outcomes among Latino persons with HIV (PWH).

Data visualizations can be effective and inclusive means for helping people understand health-related data. Yet numerous high-quality studies comparing data visualizations have yielded relatively little practical design guidance because of a lack of clarity about what communicators want their audience to accomplish. When conducting rigorous evaluations of communication (eg, applying the ISO 9186 method), describing the process simply as evaluating "comprehension" or "interpretation" of visualizations fails to do justice to [...]

Changes in cardiovascular health (CVH) during the life course are associated with future cardiovascular disease (CVD). Longitudinal clustering analysis using subgraph augmented non-negative matrix factorization (SANMF) could create phenotypic risk profiles of clustered CVH metrics.

The early stages of chronic disease typically progress slowly, so symptoms are usually only noticed until the disease is advanced. Slow progression and heterogeneous manifestations make it challenging to model the transition from normal to disease status. As patient conditions are only observed at discrete timestamps with varying intervals, an incomplete understanding of disease progression and heterogeneity affects clinical practice and drug development.

The objective of this scoping review is to map methods used to study medication safety following electronic health record (EHR) implementation. Patterns and methodological gaps can provide insight for future research design.

Surgical outcome prediction is challenging but necessary for postoperative management. Current machine learning models utilize pre- and post-op data, excluding intraoperative information in surgical notes. Current models also usually predict binary outcomes even when surgeries have multiple outcomes that require different postoperative management. This study addresses these gaps by incorporating intraoperative information into multimodal models for multiclass glaucoma surgery outcome prediction.

To construct an exhaustive Complementary and Integrative Health (CIH) Lexicon (CIHLex) to help better represent the often underrepresented physical and psychological CIH approaches in standard terminologies, and to also apply state-of-the-art natural language processing (NLP) techniques to help recognize them in the biomedical literature.

Developing targeted, culturally competent educational materials is critical for participant understanding of engagement in a large genomic study that uses computational pipelines to produce genome-informed risk assessments.

Due to insufficient smoking cessation apps for persons living with HIV, our study focused on designing and testing the Sense2Quit app, a patient-facing mHealth tool which integrated visualizations of patient information, specifically smoking use.

To determine if different formats for conveying machine learning (ML)-derived postpartum depression risks impact patient classification of recommended actions (primary outcome) and intention to seek care, perceived risk, trust, and preferences (secondary outcomes).