Search AMIA.org

Patients are increasingly being given direct access to their medical records. However, radiology reports are written for clinicians and typically contain medical jargon, which can be confusing. One solution is for radiologists to provide a "colloquial" version that is accessible to the layperson. Because manually generating these colloquial translations would represent a significant burden for radiologists, a way to automatically produce accurate, accessible patient-facing reports is desired. We propose a [...]

To develop, validate, and implement algorithms to identify diabetic retinopathy (DR) cases and controls from electronic health care records (EHRs).

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.

Unplanned readmissions following a hospitalization remain common despite significant efforts to curtail these. Wearable devices may offer help identify patients at high risk for an unplanned readmission.

Examine electronic health record (EHR) use and factors contributing to documentation burden in acute and critical care nurses.

In acupuncture therapy, the accurate location of acupoints is essential for its effectiveness. The advanced language understanding capabilities of large language models (LLMs) like Generative Pre-trained Transformers (GPTs) and Llama present a significant opportunity for extracting relations related to acupoint locations from textual knowledge sources. This study aims to explore the performance of LLMs in extracting acupoint-related location relations and assess the impact of fine-tuning on GPT's performance.

The COVID-19 pandemic emphasized the value of geospatial visual analytics for both epidemiologists and the general public. However, systems struggled to encode temporal and geospatial trends of multiple, potentially interacting variables, such as active cases, deaths, and vaccinations. We sought to ask (1) how epidemiologists interact with visual analytics tools, (2) how multiple, time-varying, geospatial variables can be conveyed in a unified view, and (3) how complex spatiotemporal encodings affect [...]

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 understand healthcare providers' experiences of using GlucoGuide, a mockup tool that integrates visual data analysis with algorithmic insights to support clinicians' use of patientgenerated data from Type 1 diabetes devices.