Citation

Apostolova E, White HA, Morris PA, Eliason DA, Velez T. AMIA Annu. Symp. Proc. 2017; 2017: 403-410.

Affiliation

Computer Technology Associates, Cardiff, CA.

Copyright

(Copyright © 2017, American Medical Informatics Association)

DOI

unavailable

PMID

29854104

Abstract

The aim of this study is to utilize the Defense and Veterans Eye Injury and Vision Registry clinical data derived from DoD and VA medical systems which include documentation of care while in combat, and develop methods for comprehensive and reliable Open Globe Injury (OGI) patient identification. In particular, we focus on the use of free-form clinical notes, since structured data, such as diagnoses or procedure codes, as found in early post-trauma clinical records, may not be a comprehensive and reliable indicator of OGIs. The challenges of the task include low incidence rate (few positive examples), idiosyncratic military ophthalmology vocabulary, extreme brevity of notes, specialized abbreviations, typos and misspellings. We modeled the problem as a text classification task and utilized a combination of supervised learning (SVMs) and word embeddings learnt in a unsupervised manner, achieving a precision of 92.50% and a recall of89.83%o. The described techniques are applicable to patient cohort identification with limited training data and low incidence rate.

Language: en