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

Showing 171 to 180 of 493 articles

Preparing for the bedside-optimizing a postpartum depression risk prediction model for clinical implementation in a health system.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We developed and externally validated a machine-learning model to predict postpartum depression (PPD) using data from electronic health records (EHRs). Effort is under way to implement the PPD prediction model within the EHR system for cli...

An interpretable predictive deep learning platform for pediatric metabolic diseases.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Metabolic disease in children is increasing worldwide and predisposes a wide array of chronic comorbid conditions with severe impacts on quality of life. Tools for early detection are needed to promptly intervene to prevent or slow the de...

Generalizing Parkinson's disease detection using keystroke dynamics: a self-supervised approach.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Passive monitoring of touchscreen interactions generates keystroke dynamic signals that can be used to detect and track neurological conditions such as Parkinson's disease (PD) and psychomotor impairment with minimal burden on the user. Ho...

Ensuring useful adoption of generative artificial intelligence in healthcare.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: This article aims to examine how generative artificial intelligence (AI) can be adopted with the most value in health systems, in response to the Executive Order on AI.

Why do users override alerts? Utilizing large language model to summarize comments and optimize clinical decision support.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To evaluate the capability of using generative artificial intelligence (AI) in summarizing alert comments and to determine if the AI-generated summary could be used to improve clinical decision support (CDS) alerts.

Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Leveraging artificial intelligence (AI) in conjunction with electronic health records (EHRs) holds transformative potential to improve healthcare. However, addressing bias in AI, which risks worsening healthcare disparities, cannot be ove...

Sustainable deployment of clinical prediction tools-a 360° approach to model maintenance.

Journal of the American Medical Informatics Association : JAMIA
BACKGROUND: As the enthusiasm for integrating artificial intelligence (AI) into clinical care grows, so has our understanding of the challenges associated with deploying impactful and sustainable clinical AI models. Complex dataset shifts resulting f...

Leveraging explainable artificial intelligence to optimize clinical decision support.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches.

Estimation of racial and language disparities in pediatric emergency department triage using statistical modeling and natural language processing.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: The study aims to assess racial and language disparities in pediatric emergency department (ED) triage using analytical techniques and provide insights into the extent and nature of the disparities in the ED setting.

Artificial intelligence predictive analytics in heart failure: results of the pilot phase of a pragmatic randomized clinical trial.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: We conducted an implementation planning process during the pilot phase of a pragmatic trial, which tests an intervention guided by artificial intelligence (AI) analytics sourced from noninvasive monitoring data in heart failure patients (...