ArticleProceedings. IEEE International Conference on Healthcare Informatics2017
Language-Based Process Phase Detection in the Trauma Resuscitation.
Article in Proceedings. IEEE International Conference on Healthcare Informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- State-of-the-art of situation recognition systems for intraoperative procedures.Medical & biological engineering & computing · 2022Review
- CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- Multimodal Attention Network for Trauma Activity Recognition from Spoken Language and Environmental Sound.Proceedings. IEEE International Conference on Healthcare Informatics · 2019Article
- Hybrid Attention based Multimodal Network for Spoken Language Classification.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2018Article
- An Analysis of Speech as a Modality for Activity Recognition during Complex Medical Teamwork.International Conference on Pervasive Computing Technologies for Healthcare : [proceedings]. International Conference on Pervasive Computing Technologies for Healthcare · 2018Article
- DEEP MULTIMODAL LEARNING FOR EMOTION RECOGNITION IN SPOKEN LANGUAGE.Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) · 2018Article
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Authors and funding
7 authors.
Funding
Abstract
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 meaning 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 remaining 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.