In longitudinal studies variables are measured repeatedly over time, leading to clustered
and correlated observations. If the goal of the study is to develop prediction models …

In Parkinson disease (PD), pathological processes and neurodegeneration begin long
before the cardinal motor symptoms develop and enable clinical diagnosis. In this prodromal …

Exploiting existing longitudinal data cohorts can bring enormous benefits to the medical
field, as many diseases have a complex and multi-factorial time-course, and start to develop …

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

Deriving disease subtypes from electronic health records (EHRs) can guide next-generation
personalized medicine. However, challenges in summarizing and representing patient data …

Increasing evidence suggests that Parkinson's disease (PD) exhibits disparate spatial and
temporal patterns of progression. Here we used a machine-learning technique—Subtype …

Background: In Parkinson's disease (PD), there is heterogeneity in the clinical presentation
and underlying biology. Research on PD subtypes aims to understand this heterogeneity …

The clinical manifestations of Parkinson's disease (PD) are characterized by heterogeneity
in age at onset, disease duration, rate of progression, and the constellation of motor versus …

The evaluation of diagnostic systems is pivotal for ensuring the deployment of high-quality
solutions, especially given the pronounced context-sensitivity of certain systems, particularly …

The progression of Parkinson's disease (PD) is heterogeneous across patients, affecting
counseling and inflating the number of patients needed to test potential neuroprotective …