Human behaviour is a dense longitudinal multi-featured measure that directly impacts the health of individuals in the short and long terms. Therefore, issues usually emerge from the insistence on performing risky behaviours, such as smoking or eating...
BACKGROUND: Secondary use of health data is a valuable source of knowledge that boosts observational studies, leading to important discoveries in the medical and biomedical sciences. The fundamental guiding principle for performing a successful obser...
Publicly accessible benchmarks that allow for assessing and comparing model performances are important drivers of progress in artificial intelligence (AI). While recent advances in AI capabilities hold the potential to transform medical practice by a...
Neutropenia and its complications are major adverse effects of cytotoxic chemotherapy. The time to recovery from neutropenia varies from patient to patient, and cannot be easily predicted even by experts. Therefore, we trained a deep learning model u...
The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety and risk-benefit profile of medications. With an incidence that has not changed over the last 30 years, ADRs are a significant source of patient morbidity, ...
The analysis of registry data has important implications for cancer monitoring, control, and treatment. In such analysis, (semi)parametric models, such as the Cox Proportional Hazards model, have been routinely adopted. In recent years, deep neural n...
Big data and (deep) machine learning have been ambitious tools in digital medicine, but these tools focus mainly on association. Intervention in medicine is about the causal effects. The average treatment effect has long been studied as a measure of ...
OBJECTIVES: We propose a communication-efficient transfer learning approach (COMMUTE) that effectively incorporates multi-site healthcare data for training a risk prediction model in a target population of interest, accounting for challenges includin...
OBJECTIVE: Biomedical named entity normalization (BNEN) is a fundamental natural language processing (NLP) task in the biomedical domain. Many representation learning-based methods have been successfully applied to BNEN in recent years. Most of them ...
BACKGROUND: Surgical context-aware systems can adapt to the current situation in the operating room and thus provide computer-aided assistance functionalities and intraoperative decision-support. To interact with the surgical team perceptively and as...