Objectives Patient representation learning refers to learning a dense mathematical
representation of a patient that encodes meaningful information from Electronic Health …

The rapid growth of omics-based data has revolutionized biomedical research and precision
medicine, allowing machine learning models to be developed for cutting-edge performance …

Generative, pre-trained transformers (GPTs, a type of" Foundation Models") have reshaped
natural language processing (NLP) through their versatility in diverse downstream tasks …

One unintended consequence of the Electronic Health Records (EHR) implementation is the
overuse of content-importing technology, such as copy-and-paste, that creates “bloated” …

Identifying patient cohorts meeting the criteria of specific phenotypes is essential in
biomedicine and particularly timely in precision medicine. Many research groups deliver …

Insufficiently precise diagnosis of clinical disease is likely responsible for many treatment
failures, even for common conditions and treatments. With a large enough dataset, it may be …

El uso de técnicas de Aprendizaje Automático (AA) en el área de la salud, específicamente
en la identificación de enfermedades cardiovasculares (IEC), ha tenido un impacto …

Abstract Electronic Health Records (EHRs) are a rich source of information that can be
leveraged for various medical applications, such as disease inference, treatment …

Medicine is undergoing a technological revolution. Understanding human health from
clinical data has major challenges from technical and practical perspectives, thus prompting …