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Natural language processing (NLP) applications typically use regular expressions that have been developed manually by human experts. Our goal is to automate both the creation and utilization of regular expressions in text classification.

To examine how patient portals contribute to health service delivery and patient outcomes. The specific aims were to examine how outcomes are produced, and how variations in outcomes can be explained.

To design, build, and evaluate a storage model able to manage heterogeneous digital imaging and communications in medicine (DICOM) images. The model must be simple, but flexible enough to accommodate variable content without structural modifications; must be effective on answering query/retrieval operations according to the DICOM standard; and must provide performance gains on querying/retrieving content to justify its adoption by image-related projects.

To evaluate state-of-the-art unsupervised methods on the word sense disambiguation (WSD) task in the clinical domain. In particular, to compare graph-based approaches relying on a clinical knowledge base with bottom-up topic-modeling-based approaches. We investigate several enhancements to the topic-modeling techniques that use domain-specific knowledge sources.

To determine whether assisted annotation using interactive training can reduce the time required to annotate a clinical document corpus without introducing bias.

As more electronic health records have become available during the last decade, we aimed to uncover recent trends in use of electronically available patient data by electronic surveillance systems for healthcare associated infections (HAIs) and identify consequences for system effectiveness.

Although electronic health records (EHRs) have the potential to provide a foundation for quality and safety algorithms, few studies have measured their impact on automated adverse event (AE) and medical error (ME) detection within the neonatal intensive care unit (NICU) environment.

To develop a generalizable method for identifying patient cohorts from electronic health record (EHR) data-in this case, patients having dialysis-that uses simple information retrieval (IR) tools.

Children are a vulnerable population in the operating room, and are particularly at risk of complications from unanticipated hemorrhage. The decision to prepare blood products prior to surgery varies depending on the personal experience of the clinician caring for the patient. We present the first application of a data visualization technique to study large datasets in the context of blood product transfusions at a tertiary pediatric hospital. The visual analytical [...]