Transforming unstructured voice and text data into insight for paramedic emergency service using recurrent and convolutional neural networks

Kyongsik Yun; Thomas Lu; Alexander Huyen

doi:10.1117/12.2558157

20 May 2020 Transforming unstructured voice and text data into insight for paramedic emergency service using recurrent and convolutional neural networks

Kyongsik Yun, Thomas Lu, Alexander Huyen

Author Affiliations +

Proceedings Volume 11400, Pattern Recognition and Tracking XXXI; 1140006 (2020) https://doi.org/10.1117/12.2558157
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Paramedics often have to make lifesaving decisions within a limited time in an ambulance. They sometimes ask the doctor for additional medical instructions, during which valuable time passes for the patient. This study aims to automatically fuse voice and text data to provide tailored situational awareness information to paramedics. To train and test speech recognition models, we built a bidirectional deep recurrent neural network (long short-term memory (LSTM)). Then we used convolutional neural networks on top of custom-trained word vectors for sentence-level classification tasks. Each sentence is automatically categorized into four classes, including patient status, medical history, treatment plan, and medication reminder. Subsequently, incident reports were automatically generated to extract keywords and assist paramedics and physicians in making decisions. The proposed system found that it could provide timely medication notifications based on unstructured voice and text data, which was not possible in paramedic emergencies at present. In addition, the automatic incident report generation provided by the proposed system improves the routine but error-prone tasks of paramedics and doctors, helping them focus on patient care.

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Kyongsik Yun, Thomas Lu, and Alexander Huyen "Transforming unstructured voice and text data into insight for paramedic emergency service using recurrent and convolutional neural networks", Proc. SPIE 11400, Pattern Recognition and Tracking XXXI, 1140006 (20 May 2020); https://doi.org/10.1117/12.2558157

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KEYWORDS

Speech recognition

Data modeling

Neural networks

Optical character recognition

Convolutional neural networks

Human-machine interfaces

Chest

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