Title

Language-Based Process Phase Detection in the Trauma Resuscitation

Document Type

Conference Proceeding

Publication Date

9-8-2017

Journal

Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017

DOI

10.1109/ICHI.2017.50

Keywords

Deep learning; LSTM; Process phase detection; Semantic representation; Verbal communication logs

Abstract

© 2017 IEEE. Process phase detection has been widely used in surgical process modeling (SPM) to track process progression. These studies mostly used video and embedded sensor data, but spoken language also provides rich semantic information directly related to process progression. We present a long-short term memory (LSTM) deep learning model to predict trauma resuscitation phases using verbal communication logs. We first use an LSTM to extract the sentence meaning representations, and then sequentially feed them into another LSTM to extract the mean-ing of a sentence group within a time window. This information is ultimately used for phase prediction. We used 24 manually-transcribed trauma resuscitation cases to train, and the remain-ing 6 cases to test our model. We achieved 79.12% accuracy, and showed performance advantages over existing visual-audio systems for critical phases of the process. In addition to language information, we evaluated a multimodal phase prediction structure that also uses audio input. We finally identified the challenges of substituting manual transcription with automatic speech recognition in trauma resuscitation.

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