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 …

Personal thermal comfort prediction modeling has become a trending topic in efforts to
improve individual indoor comfort, a notion that is closely related to the design and …

Abstract Objective Coronavirus disease 2019 (COVID-19) poses societal challenges that
require expeditious data and knowledge sharing. Though organizational clinical data are …

Electronic Health Records (EHRs) allows patients to control, share, and manage their health
records among family members, friends, and healthcare service providers using an open …

Deep learning methods especially recurrent neural network based models have
demonstrated early success in disease risk prediction on longitudinal patient data. Existing …

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 …

Objective Temporal electronic health records (EHRs) contain a wealth of information for
secondary uses, such as clinical events prediction and chronic disease management …

The goal of diagnosis prediction task is to predict the future health information of patients
from their historical Electronic Healthcare Records (EHR). The most important and …

Automatic ICD coding is defined as assigning disease codes to electronic medical records
(EMRs). Existing methods usually apply label attention with code representations to match …