Abstract Background: Natural Language Processing (NLP) is widely used to extract clinical
insights from Electronic Health Records (EHRs). However, the lack of annotated data …

Objective Data-driven population segmentation is commonly used in clinical settings to
separate the heterogeneous population into multiple relatively homogenous groups with …

Objective Accurate and rapid phenoty** is a prerequisite to leveraging electronic health
records for biomedical research. While early phenoty** relied on rule-based algorithms …

People with a history of injecting drug use are a priority for eliminating blood-borne viruses
and sexually transmissible infections. Identifying them for disease surveillance in electronic …

Objective Accurately identifying clinical phenotypes from Electronic Health Records (EHRs)
provides additional insights into patients' health, especially when such information is …

The rapid growth of electronic health record (EHR) datasets opens up promising
opportunities to understand human diseases in a systematic way. However, effective …

Objective High-throughput phenoty** will accelerate the use of electronic health records
(EHRs) for translational research. A critical roadblock is the extensive medical supervision …

Increasing availability of electronic health databases capturing real-world experiences with
medical products has garnered much interest in their use for pharmacoepidemiologic and …

Succinct clinical documentation is vital to effective twenty-first-century healthcare. Recent
changes in outpatient and inpatient evaluation and management (E/M) guidelines have …

Background Urologic research often requires data abstraction from unstructured text
contained within the electronic health record. A number of natural language processing …