Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction.

Background: Machine learning and deep learning are powerful tools for analyzing electronic health records (EHRs) in healthcare research. Although family health history has been recognized as a major predictor for a wide spectrum of diseases, research has so far adopted a limited view of family relations, essentially treating patients as independent samples in the analysis.

Methods: To address this gap, we present ALIGATEHR, which models inferred family relations in a graph attention network augmented with an attention-based medical ontology representation, thus accounting for the complex influence of genetics, shared environmental exposures, and disease dependencies.

Deep learning-based patient stratification for prognostic enrichment of clinical dementia trials.

Dementia probably due to Alzheimer's disease is a progressive condition that manifests in cognitive decline and impairs patients' daily life. Affected patients show great heterogeneity in their symptomatic progression, which hampers the identification of efficacious treatments in clinical trials. Using artificial intelligence approaches to enable clinical enrichment trials serves a promising avenue to identify treatments.

How do citizens with low health insurance literacy choose a health insurance policy in the Netherlands? An interview study.

Various countries have implemented a choice-based health insurance system. For such systems to function as intended, it is crucial that all citizens have the opportunity to make well-informed decisions with regard to their health insurance policy. There is, however, ample research evidence to suggest that many citizens may lack the required skills to do so, thus increasing the likelihood of suboptimal insurance choices and incurring unexpected costs.

Blood-based biomarkers for early frailty are sex-specific: validation of a combined in silico prediction and data-driven approach.

Frailty is characterized by loss of physical function and is preferably diagnosed at an early stage (e.g., during pre-frailty).