Search AMIA.org

The Australian Cancer Atlas (ACA) aims to provide small-area estimates of cancer incidence and survival in Australia to help identify and address geographical health disparities. We report on the 21-month user-centered design study to visualize the data, in particular, the visualization of the estimate uncertainty for multiple audiences.

To address the need for interactive visualization tools and databases in characterizing multimorbidity patterns across different populations, we developed the Phenome-wide Multi-Institutional Multimorbidity Explorer (PheMIME). This tool leverages three large-scale EHR systems to facilitate efficient analysis and visualization of disease multimorbidity, aiming to reveal both robust and novel disease associations that are consistent across different systems and to provide insight for enhancing personalized healthcare strategies.

Active learning (AL) has rarely integrated diversity-based and uncertainty-based strategies into a dynamic sampling framework for clinical named entity recognition (NER). Machine-assisted annotation is becoming popular for creating gold-standard labels. This study investigated the effectiveness of dynamic AL strategies under simulated machine-assisted annotation scenarios for clinical NER.

Patients with chronic illnesses, including kidney disease, consider their sense of normalcy when evaluating their health. Although this concept is a key indicator of their self-determined well-being, they struggle to understand if their experience is typical. To address this challenge, we set out to explore how to design personal health visualizations that aid participants in better understanding their experiences post-transplant, identifying barriers to normalcy, and achieving their desired medical outcomes.

To enhance and evaluate the quality of PubMed search results for Social Determinants of Health (SDoH) through the addition of new SDoH terms to Medical Subject Headings (MeSH).

To understand the landscape of privacy preserving record linkage (PPRL) applications in public health, assess estimates of PPRL accuracy and privacy, and evaluate factors for PPRL adoption.

Recently, deep learning medical image analysis in orthopedics has become highly active. However, progress has been restricted by the absence of large-scale and standardized ground-truth images. To the best of our knowledge, this study is the first to propose an innovative solution, namely a deep few-shot image augmentation pipeline, that addresses this challenge by synthetically generating knee radiographs for training downstream tasks, with a specific focus on knee osteoarthritis Kellgren-Lawrence [...]

Clinical Data Warehouses (CDW) are the designated infrastructures to enable access and analysis of large quantities of electronic health record data. Building and managing such systems implies extensive "data work" and coordination between multiple stakeholders. Our study focuses on the challenges these stakeholders face when designing, operating, and ensuring the durability of CDWs for research.

Well-designed electronic health records (EHRs) training programs for clinical practice are known to be valuable. Training programs should be role-specific and there is a need to identify key implementation factors of EHR training programs for nurses. This scoping review (1) characterizes the EHR training programs used and (2) identifies their implementation facilitators and barriers.