Search AMIA.org

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

In a growing interdisciplinary field like biomedical informatics, information dissemination and citation trends are changing rapidly due to many factors. To understand these factors better, we analyzed the evolution of the number of articles per major biomedical informatics topic, download/online view frequencies, and citation patterns (using Web of Science) for articles published from 2009 to 2012 in JAMIA. The number of articles published in JAMIA increased significantly from 2009 to [...]

To develop scalable informatics infrastructure for normalization of both structured and unstructured electronic health record (EHR) data into a unified, concept-based model for high-throughput phenotype extraction.

Extracting comorbidity information is crucial for phenotypic studies because of the confounding effect of comorbidities. We developed an automated method that accurately determines comorbidities from electronic medical records. Using a modified version of the Charlson comorbidity index (CCI), two physicians created a reference standard of comorbidities by manual review of 100 admission notes. We processed the notes using the MedLEE natural language processing system, and wrote queries to extract comorbidities [...]

To develop algorithms to improve efficiency of patient phenotyping using natural language processing (NLP) on text data. Of a large number of note titles available in our database, we sought to determine those with highest yield and precision for psychosocial concepts.

To construct and validate billing code algorithms for identifying patients with peripheral arterial disease (PAD).

We define and validate an architecture for systems that identify patient cohorts for clinical trials from multiple heterogeneous data sources. This architecture has an explicit query model capable of supporting temporal reasoning and expressing eligibility criteria independently of the representation of the data used to evaluate them.