Abstract
We present a section boundary detection framework specifically for clinical dictations. Detection is cast as a semi-supervised binary tagging problem and solved using a neural network model composed of a stack of embeddings, unidirectional long-short term memory units (LSTMs), and sigmoid outputs. Physicians’ dictations documenting clinical encounters are typically transcribed using automatic speech recognition (ASR) followed by a post-processor (PP) to transform the raw text into written reports. Section boundary detection can be performed directly upon the raw text to better anticipate the post-processing stage: we describe an architecture for real-time (“live”) ASR use in which sections detected by the tagger are sent individually to a machine translation-based PP (for which continuous execution in real time would not be possible). Our implementation of section detection makes viable the use of a sophisticated machine learning PP in a live dictation paradigm.
N. Sadoughi and G.P. Finley—The authors made equal contributions to this paper.
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Sadoughi, N. et al. (2018). Detecting Section Boundaries in Medical Dictations: Toward Real-Time Conversion of Medical Dictations to Clinical Reports. In: Karpov, A., Jokisch, O., Potapova, R. (eds) Speech and Computer. SPECOM 2018. Lecture Notes in Computer Science(), vol 11096. Springer, Cham. https://doi.org/10.1007/978-3-319-99579-3_58
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