Identification of patient name references within medical documents using semantic selectional restrictions.
De-identification of a patient's personal data from medical records is a protective legal requirement imposed before medical documents can be used for research purposes or transferred to other healthcare providers (e.g., teachers, students, tele-consultations). This de-identification process is tedious if performed manually, and is known to be quite faulty in direct search and replace strategies [9]. In this paper, we report on the identification step of this process. The proposed algorithm is based on estimating the fitness of candidate patient name references to a set of semantic selectional restrictions. The semantic restrictions place tight contextual requirements upon candidate words in the report text and are determined automatically from a manually tagged corpus of training reports. Maximum entropy classifiers are used to provide a probabilistic measure of the belief of a given candidate token to a given semantic restriction. We report on the design and preliminary evaluation of the system within the do-main of pediatric urology.
Taira
Ricky K
RK
Telemedicine Division, Department of Radiology, UCLA School of Medicine, Los Angeles, CA, USA.
Bui
Alex A T
AA
Kangarloo
Hooshang
H
D000465:Algorithms; D006801:Humans; D008499:Medical Records; D009275:Names; D010372:Pediatrics; D012660:Semantics; D014572:Urology