Abstract Machine learning models for healthcare commonly use binary indicator variables to
represent the diagnosis of specific health conditions in medical records. However, in …

The identification of medical concepts, their attributes and the relations between concepts in
a large corpus of Electroencephalography (EEG) reports is a crucial step in the development …

Objective: To demonstrate that a semi-automated approach to health data abstraction
provides significant efficiencies and high accuracy. Background: Surgical outcome …

Machine Learning (ML) is widely used to automatically extract meaningful information from
Electronic Health Records (EHR) to support operational, clinical, and financial decision …

In medical data analysis, addressing challenges from high-dimensional datasets is crucial
due to issues related to computational complexity, resource utilization, and model …

One of the persistent challenges in building machine-learned models for mobile health
applications of fine-grained activity is the generation of accurate annotations with well …

The worldwide adoption of electronic health records (EHRs) to document patient data
enables the use of big-data methods to harness the medical information contained therein …

Suicide is a serious public health problem in the United States. Numerous studies have
shown that many suicides occur in individuals in contact with the healthcare system, which …