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Deep-learning techniques, particularly the Transformer model, have shown great potential in enhancing the prediction performance of longitudinal health records. Previous methods focused on fixed-time risk prediction, however, time-to-event prediction is often more appropriate for clinical scenarios. Here, we present STRAFE, a generalizable survival analysis Transformer-based architecture for electronic health records.

Despite federally mandated collection of sex and gender demographics in the electronic health record (EHR), longitudinal assessments are lacking. We assessed sex and gender demographic field utilization using EHR metadata.

Understand public comfort with the use of different data types for predictive models.

To report on clinical informatics (CI) fellows' job search and early careers.

Effective communication amongst healthcare workers simultaneously promotes optimal patient outcomes when present and is deleterious to outcomes when absent. The advent of electronic health record (EHR)-embedded secure instantaneous messaging systems has provided a new conduit for provider communication. This manuscript describes the experience of one academic medical center with deployment of one such system (Secure Chat).

Artificial intelligence (AI) detects heart disease from images of electrocardiograms (ECGs). However, traditional supervised learning is limited by the need for large amounts of labeled data. We report the development of Biometric Contrastive Learning (BCL), a self-supervised pretraining approach for label-efficient deep learning on ECG images.

Evaluate the impact of community tele-paramedicine (CTP) on patient experience and satisfaction relative to community-level indicators of health disparity.

Large language models (LLMs) have shown impressive ability in biomedical question-answering, but have not been adequately investigated for more specific biomedical applications. This study investigates ChatGPT family of models (GPT-3.5, GPT-4) in biomedical tasks beyond question-answering.

To measure pediatrician adherence to evidence-based guidelines in the treatment of young children with attention-deficit/hyperactivity disorder (ADHD) in a diverse healthcare system using natural language processing (NLP) techniques.

We applied a user experience (UX) design approach to clinical decision support (CDS) tool development for the specific use case of pediatric asthma. Our objective was to understand physicians' workflows, decision-making processes, barriers (ie, pain points), and facilitators to increase usability of the tool.