TY - GEN
T1 - IMACS - An interactive cognitive assistant module for cardiac arrest cases in emergency medical service
T2 - 18th ACM Conference on Embedded Networked Sensor Systems, SenSys 2020
AU - Rahman, M. Arif
AU - Preum, Sarah
AU - Stankovic, John A.
AU - Jia, Leon
AU - Mirza, Eimara
AU - Williams, Ronald
AU - Alemzadeh, Homa
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/11/16
Y1 - 2020/11/16
N2 - IMACS is an intelligent, interactive cognitive assistant dedicated to cardiac arrest cases in Emergency Medical Service (EMS). EMS providers deal with many cardiac cases. IMACS interacts with EMS providers in real-time and collects vital information from the providers' conversation, including names of interventions, timestamps of interventions, and dosage amount. Throughout the process, IMACS provides necessary reminders and creates a summary report afterward. Using the dynamic behavioral model of two different cardiac arrest recovery protocols, we have developed a critical risk-index based approach to provide time-sensitive feedback and suggest alternatives to the providers in real-time. Our experiments reveal an F1-score of 83% with 300 test cases. A qualitative study also reflects that seven out of ten of the EMS providers rate the system as very helpful in correctly executing cardiac arrest EMS protocols.
AB - IMACS is an intelligent, interactive cognitive assistant dedicated to cardiac arrest cases in Emergency Medical Service (EMS). EMS providers deal with many cardiac cases. IMACS interacts with EMS providers in real-time and collects vital information from the providers' conversation, including names of interventions, timestamps of interventions, and dosage amount. Throughout the process, IMACS provides necessary reminders and creates a summary report afterward. Using the dynamic behavioral model of two different cardiac arrest recovery protocols, we have developed a critical risk-index based approach to provide time-sensitive feedback and suggest alternatives to the providers in real-time. Our experiments reveal an F1-score of 83% with 300 test cases. A qualitative study also reflects that seven out of ten of the EMS providers rate the system as very helpful in correctly executing cardiac arrest EMS protocols.
UR - http://www.scopus.com/inward/record.url?scp=85097566228&partnerID=8YFLogxK
U2 - 10.1145/3384419.3430451
DO - 10.1145/3384419.3430451
M3 - Conference article
AN - SCOPUS:85097566228
T3 - SenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems
SP - 621
EP - 622
BT - SenSys 2020 - Proceedings of the 2020 18th ACM Conference on Embedded Networked Sensor Systems
PB - Association for Computing Machinery, Inc
Y2 - 16 November 2020 through 19 November 2020
ER -