| Literature DB >> 24551400 |
Andrew R Post1, Tahsin Kurc1, Richie Willard1, Himanshu Rathod1, Michel Mansour1, Akshatha Kalsanka Pai1, William M Torian1, Sanjay Agravat1, Suzanne Sturm1, Joel H Saltz1.
Abstract
Temporal abstraction, a method for specifying and detecting temporal patterns in clinical databases, is very expressive and performs well, but it is difficult for clinical investigators and data analysts to understand. Such patterns are critical in phenotyping patients using their medical records in research and quality improvement. We have previously developed the Analytic Information Warehouse (AIW), which computes such phenotypes using temporal abstraction but requires software engineers to use. We have extended the AIW's web user interface, Eureka! Clinical Analytics, to support specifying phenotypes using an alternative model that we developed with clinical stakeholders. The software converts phenotypes from this model to that of temporal abstraction prior to data processing. The model can represent all phenotypes in a quality improvement project and a growing set of phenotypes in a multi-site research study. Phenotyping that is accessible to investigators and IT personnel may enable its broader adoption.Entities:
Mesh：
Year: 2013 PMID： 24551400 PMCID： PMC3900137
Source DB: PubMed Journal: AMIA Annu Symp Proc ISSN： 1559-4076