Search AMIA.org

A comprehensive and machine-understandable cancer drug-side effect (drug-SE) relationship knowledge base is important for in silico cancer drug target discovery, drug repurposing, and toxicity predication, and for personalized risk-benefit decisions by cancer patients. While US Food and Drug Administration (FDA) drug labels capture well-known cancer drug SE information, much cancer drug SE knowledge remains buried the published biomedical literature. We present a relationship extraction approach to extract cancer drug-SE pairs [...]

Epilepsy encompasses an extensive array of clinical and research subdomains, many of which emphasize multi-modal physiological measurements such as electroencephalography and neuroimaging. The integration of structured, unstructured, and signal data into a coherent structure for patient care as well as clinical research requires an effective informatics infrastructure that is underpinned by a formal domain ontology.

To determine whether the knowledge contained in a rich corpus of local terms mapped to LOINC (Logical Observation Identifiers Names and Codes) could be leveraged to help map local terms from other institutions.

To develop a conceptual framework for the design of an in-home monitoring system (IMS) based on the requirements of older adults with vision impairment (VI), informal caregivers and eye-care rehabilitation professionals.

To better understand the relationship between online health-seeking behaviors and in-world healthcare utilization (HU) by studies of online search and access activities before and after queries that pursue medical professionals and facilities.

To develop and validate an accurate method to identify patients with chronic pain using electronic health records (EHR) data at a multisite community health center.

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).