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Paramedics often have to make lifesaving decisions within a limited time inan ambulance. They sometimes ask the doctor for additional medicalinstructions, during which valuable time passes for the patient. This studyaims to automatically fuse voice and text data to provide tailored situationalawareness information to paramedics. To train and test speech recognitionmodels, we built a bidirectional deep recurrent neural network (long short-termmemory (LSTM)). Then we used convolutional neural networks on top ofcustom-trained word vectors for sentence-level classification tasks. Eachsentence is automatically categorized into four classes, including patientstatus, medical history, treatment plan, and medication reminder. Subsequently,incident reports were automatically generated to extract keywords and assistparamedics and physicians in making decisions. The proposed system found thatit could provide timely medication notifications based on unstructured voiceand text data, which was not possible in paramedic emergencies at present. Inaddition, the automatic incident report generation provided by the proposedsystem improves the routine but error-prone tasks of paramedics and doctors,helping them focus on patient care.

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