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Healthc Inform Res > Volume 8(4); 2002 > Article
Journal of Korean Society of Medical Informatics 2002;8(4):11-20.
DOI: https://doi.org/10.4258/jksmi.2002.8.4.11    Published online December 31, 2002.
Correction System of a Mis-recognized Medical Vocabulary of Speech-based Electronic Medical Record
Hwa Jeong Seo, Ju Han Kim
Seoul National University Biomedical Informatics, Seoul National University College of Medicine, Korea.
Abstract

Speech recognition as an input tool for electronic medical record enables efficient data entry at the point of care. We evaluated the speech recognition accuracy of IBM ViaVoiceTM for doctor-patient dialogues and for pronounced medical vocabularies. The recognition accuracy for doctor-patient dialogues was 95.4%, while that for pronounced medical vocabularies was 55.1%. In order to put speech-based electronic medical record to practical use, mis-recognized vocabulary must be significantly corrected. This paper describes a correction system for mis-recognized medical vocabulary for speech recognition-enabled electronic medical record. The correction system is composed of an extraction and a correction steps. In the extraction step, hamming distance between a parsed substring and the nearest medical vocabulary in the vocabulary database greater than 50% of the length of the substring was used to determine if the substring is a possible mis-recognized medical vocabulary. In the correction step, possible mis-recognized medical vocabularies are scored such that when both the code and location of a syllable is the same with those of a medical vocabulary found in our database, +5 is given and when the code is the same but the location is not, +1 is given. The medical vocabulary with the highest score in the database is used as the correction for the mis-recognized one. When 33 patient-doctor dialogues with 33 medical vocabularies were tested for three times by six testees (i.e., 33 × 6 × 3 = 594 sentences), 94% of the mis-recognized words were correctly detected and repaired. Poor recognition performance for hard medical vocabularies can be markedly improved by the mis-recognized medical vocabulary correction system.

Key Words: EMR(Electronica Medical Record), Speech Recognition, Data Entry, NLP(Natural Language Processing)


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