In the realm of medicine, artificial intelligence (AI) has emerged as a transformative force,
harnessing the power to convert raw data into meaningful insights. Rather than supplanting …

Deep neural networks (DNN) have fundamentally revolutionized the artificial intelligence
(AI) field. The transformer model is a type of DNN that was originally used for the natural …

Background A large language model (LLM) is a machine learning model inferred from text
data that captures subtle patterns of language use in context. Modern LLMs are based on …

In this study, we present a novel approach for predicting interventions for patients in the
intensive care unit using a multivariate time series graph convolutional neural network. Our …

The reconstruction of patient paths—ie, their temporally ordered diagnoses, diagnostic
procedures, and the resulting treatments—has shown great potential for gaining new …

Electronic Health Records (EHRs) play an important role in the healthcare system. However,
their complexity and vast volume pose significant challenges to data interpretation and …

Precision medicine can be defined as providing the right treatment to the right patient at the
right time, and it requires the ability to identify clinically relevant patient subgroups with high …

The complex nature of disease mechanisms and the variability of patient symptoms present
significant obstacles in developing effective diagnostic tools. Although machine learning has …

Predicting outcomes for cancer patients initiating chemotherapy is essential for care
planning and offers potential to support clinical and health policy decision-making. Existing …

The widespread adoption of Electronic Health Records (EHRs) and deep learning,
particularly through Self-Supervised Representation Learning (SSRL) for categorical data …