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Electronic medical records (EMRs) can support medical research and discovery, but privacy risks limit the sharing of such data on a wide scale. Various approaches have been developed to mitigate risk, including record simulation via generative adversarial networks (GANs). While showing promise in certain application domains, GANs lack a principled approach for EMR data that induces subpar simulation. In this article, we improve EMR simulation through a novel pipeline that [...]

The study sought to assess the feasibility of nationwide chronic disease surveillance using data aggregated through a multisite collaboration of customers of the same electronic health record (EHR) platform across the United States.

Many policymakers and advocates assume that directed and query-based health information exchange (HIE) work together to meet organizations' interoperability needs, but this is not grounded in a substantial evidence base. This study sought to clarify the relationship between the usage of these 2 approaches to HIE.

Detecting adverse drug events (ADEs) and medications related information in clinical notes is important for both hospital medical care and medical research. We describe our clinical natural language processing (NLP) system to automatically extract medical concepts and relations related to ADEs and medications from clinical narratives. This work was part of the 2018 National NLP Clinical Challenges Shared Task and Workshop on Adverse Drug Events and Medication Extraction.

This article summarizes the preparation, organization, evaluation, and results of Track 2 of the 2018 National NLP Clinical Challenges shared task. Track 2 focused on extraction of adverse drug events (ADEs) from clinical records and evaluated 3 tasks: concept extraction, relation classification, and end-to-end systems. We perform an analysis of the results to identify the state of the art in these tasks, learn from it, and build on it.

To develop a natural language processing system that identifies relations of medications with adverse drug events from clinical narratives. This project is part of the 2018 n2c2 challenge.

Identification of drugs, associated medication entities, and interactions among them are crucial to prevent unwanted effects of drug therapy, known as adverse drug events. This article describes our participation to the n2c2 shared-task in extracting relations between medication-related entities in electronic health records.

An adverse drug event (ADE) refers to an injury resulting from medical intervention related to a drug including harm caused by drugs or from the usage of drugs. Extracting ADEs from clinical records can help physicians associate adverse events to targeted drugs.

This article describes an ensembling system to automatically extract adverse drug events and drug related entities from clinical narratives, which was developed for the 2018 n2c2 Shared Task Track 2.