Abstract Background: Electronic Health Records (EHRs) aggregate diverse information at
the patient level, holding a trajectory representative of the evolution of the patient health …

Physicians use electronic health records to monitor patient health and make more accurate
prognoses, diagnoses and clinical decisions. However, with the ever increasing amounts of …

The problem of analyzing patient trajectories is fundamental to our ability to understand and
characterize diseases and how we treat them in our hospitals, and to devise and explore …

In digital medicine, patient data typically record health events over time (eg, through
electronic health records, wearables, or other sensing technologies) and thus form unique …

Modeling with longitudinal electronic health record (EHR) data proves challenging given the
high dimensionality, redundancy, and noise captured in EHR. In order to improve precision …

The advent of the Internet era has led to an explosive growth in the Electronic Health
Records (EHR) in the past decades. The EHR data can be regarded as a collection of …

Proactive analysis of patient pathways helps healthcare providers anticipate treatment-
related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage …

A hospital readmission is when a patient who was discharged from the hospital is admitted
again for the same or related care within a certain period. Hospital readmissions are a …

Healthcare delivery systems are widely accepted as socio-technical systems. Unlike other
socio-technical systems, healthcare systems leave very little decision-making to technical …

Abstract Motivation Electronic health records (EHRs) are quickly becoming omnipresent in
healthcare, but interoperability issues and technical demands limit their use for biomedical …