TY - GEN
T1 - GRACE
T2 - 32nd Innovative Applications of Artificial Intelligence Conference, IAAI 2020
AU - Rahman, M. Arif
AU - Preum, Sarah M.
AU - Williams, Ronald
AU - Alemzadeh, Homa
AU - Stankovic, John A.
N1 - Publisher Copyright:
© 2020 Proceedings of the 30th Innovative Applications of Artificial Intelligence Conference, IAAI 2018. All Rights Reserved.
PY - 2020
Y1 - 2020
N2 - EMS (emergency medical service) plays an important role in saving lives in emergency and accident situations. When first responders, including EMS providers and firefighters, arrive at an incident, they communicate with the patients (if conscious), family members and other witnesses, other first responders, and the command center. The first responders utilize a microphone and headset to support these communications. After the incident, the first responders are required to document the incident by filling out a form. Today, this is performed manually. Manual documentation of patient summary report is time-consuming, tedious, and error-prone. We have addressed these form filling problems by transcribing the audio from the scene, identifying the relevant information from all the conversations, and automatically filling out the form. Informal survey of first responders indicate that this application would be exceedingly helpful to them. Results show that we can fill out a model summary report form with an F1 score as high as 94%, 78%, 96%, and 83% when the data is noise-free audio, noisy audio, noise-free textual narratives, and noisy textual narratives, respectively.
AB - EMS (emergency medical service) plays an important role in saving lives in emergency and accident situations. When first responders, including EMS providers and firefighters, arrive at an incident, they communicate with the patients (if conscious), family members and other witnesses, other first responders, and the command center. The first responders utilize a microphone and headset to support these communications. After the incident, the first responders are required to document the incident by filling out a form. Today, this is performed manually. Manual documentation of patient summary report is time-consuming, tedious, and error-prone. We have addressed these form filling problems by transcribing the audio from the scene, identifying the relevant information from all the conversations, and automatically filling out the form. Informal survey of first responders indicate that this application would be exceedingly helpful to them. Results show that we can fill out a model summary report form with an F1 score as high as 94%, 78%, 96%, and 83% when the data is noise-free audio, noisy audio, noise-free textual narratives, and noisy textual narratives, respectively.
UR - http://www.scopus.com/inward/record.url?scp=85095783800&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85095783800
T3 - Proceedings of the 32nd Innovative Applications of Artificial Intelligence Conference, IAAI 2020
SP - 13356
EP - 13362
BT - Proceedings of the 32nd Innovative Applications of Artificial Intelligence Conference, IAAI 2020
A2 - Puri, Ruchir
A2 - Yorke-Smith, Neil
PB - AAAI Press
Y2 - 9 February 2020 through 11 February 2020
ER -