Research Article
A Detection of Informal Abbreviations from Free Text Medical Notes Using Deep Learning
Author(s):
Lukman Heryawan*, Osamu Sugiyama, Goshiro Yamamoto, Purnomo Husnul Khotimah, Luciano H. O. Santos, Kazuya Okamoto and Tomohiro Kuroda
Background: To parse free text medical notes into structured data such as disease names, drugs, procedures, and other important medical information first, it is necessary to detect medical entities. It is important for an Electronic Medical Record (EMR) to have structured data with semantic interoperability to serve as a seamless communication platform whenever a patient migrates from one physician to another. However, in free text notes, medical entities are often expressed using informal abbreviations. An informal abbreviation is a non-standard or undetermined abbreviation, made in diverse writing styles, which may burden the semantic interoperability between EMR systems. Therefore, a detection of informal abbreviations is required to tackle this issue.
Objectives: We attempt to achieve highly reliable detection of informal abbreviations mad.. Read More»