  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.
