Existing machine learning methods typically assume consistency in how semantically
equivalent information is encoded. However, the way information is recorded in databases …

Increased availability of Electronic Health Record (EHR) data provides unique opportunities
for improving the quality of health services. In this study, we couple EHRs with the advanced …

In this paper, we present a method to dynamically estimate the probability of mortality inside
the Intensive Care Unit (ICU) by combining heterogeneous data. We propose a method …

Electronic health records (EHR) are collected as a routine part of healthcare delivery, and
have great potential to be utilized to improve patient health outcomes. They contain multiple …

Background Scalable and accurate health outcome prediction using electronic health record
(EHR) data has gained much attention in research recently. Previous machine learning …

Clinical outcome prediction based on Electronic Health Record (EHR) helps enable early
interventions for high-risk patients, and is thus a central task for smart healthcare …

Objective In applying machine learning (ML) to electronic health record (EHR) data, many
decisions must be made before any ML is applied; such preprocessing requires substantial …

The wide implementation of electronic health record (EHR) systems facilitates the collection
of large-scale health data from real clinical settings. Despite the significant increase in …

Background Clinical decision support systems assist physicians in interpreting complex
patient data. However, they typically operate on a per-patient basis and do not exploit the …

Proper representations of medical concepts such as diagnosis, medication, procedure
codes and visits from Electronic Health Records (EHR) has broad applications in healthcare …