AIMC Journal:
Journal of the American Medical Informatics Association : JAMIA

Showing 291 to 300 of 493 articles

Leveraging conversational technology to answer common COVID-19 questions.

Journal of the American Medical Informatics Association : JAMIA
The rapidly evolving science about the Coronavirus Disease 2019 (COVID-19) pandemic created unprecedented health information needs and dramatic changes in policies globally. We describe a platform, Watson Assistant (WA), which has been used to develo...

Importance-aware personalized learning for early risk prediction using static and dynamic health data.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Accurate risk prediction is important for evaluating early medical treatment effects and improving health care quality. Existing methods are usually designed for dynamic medical data, which require long-term observations. Meanwhile, import...

STAN: spatio-temporal attention network for pandemic prediction using real-world evidence.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We aim to develop a hybrid model for earlier and more accurate predictions for the number of infected cases in pandemics by (1) using patients' claims data from different counties and states that capture local disease status and medical re...

The risk of racial bias while tracking influenza-related content on social media using machine learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Machine learning is used to understand and track influenza-related content on social media. Because these systems are used at scale, they have the potential to adversely impact the people they are built to help. In this study, we explore t...

Predicting outcomes in central venous catheter salvage in pediatric central line-associated bloodstream infection.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Central line-associated bloodstream infections (CLABSIs) are a common, costly, and hazardous healthcare-associated infection in children. In children in whom continued access is critical, salvage of infected central venous catheters (CVCs)...

Recommendations for the safe, effective use of adaptive CDS in the US healthcare system: an AMIA position paper.

Journal of the American Medical Informatics Association : JAMIA
The development and implementation of clinical decision support (CDS) that trains itself and adapts its algorithms based on new data-here referred to as Adaptive CDS-present unique challenges and considerations. Although Adaptive CDS represents an ex...

UMLS-based data augmentation for natural language processing of clinical research literature.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The study sought to develop and evaluate a knowledge-based data augmentation method to improve the performance of deep learning models for biomedical natural language processing by overcoming training data scarcity.

Application of Bayesian networks to generate synthetic health data.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This study seeks to develop a fully automated method of generating synthetic data from a real dataset that could be employed by medical organizations to distribute health data to researchers, reducing the need for access to real data. We h...

Trust and medical AI: the challenges we face and the expertise needed to overcome them.

Journal of the American Medical Informatics Association : JAMIA
Artificial intelligence (AI) is increasingly of tremendous interest in the medical field. How-ever, failures of medical AI could have serious consequences for both clinical outcomes and the patient experience. These consequences could erode public tr...

A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions.

Journal of the American Medical Informatics Association : JAMIA
Unplanned hospital readmissions are a burden to patients and increase healthcare costs. A wide variety of machine learning (ML) models have been suggested to predict unplanned hospital readmissions. These ML models were often specifically trained on ...