IMACS - an <u>i</u>nteractive cognitive assistant <u>m</u>odule for <u>c</u>ardiac <u>a</u>rrest cases in emergency medical <u>s</u>ervice: demo abstract
Authors: 
M Arif Rahman, Sarah Preum, John A. Stankovic, Leon Jia, Eimara Mirza, Ronald Williams, Homa AlemzadehAuthors Info & Claims
M Arif Rahman, Sarah Preum, John A. Stankovic, Leon Jia, Eimara Mirza, Ronald Williams, Homa AlemzadehAuthors Info & ClaimsPages 621 - 622
Abstract
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.
References
[1]
Homa Alemzadeh and Murthy Devarakonda. 2017. An NLP-based cognitive system for disease status identification in electronic health records. In 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, 89--92.
[2]
Google. [n.d.]. Speech-to-Text API by Google. In https://groups.google.com/forum/#!topic/nltk-users/CS2fCFxvu1I.
[3]
Robert Graham, Margaret A McCoy, Andrea M Schultz, et al. 2015. Understanding the Public Health Burden of Cardiac Arrest: The Need for National Surveillance. In Strategies to Improve Cardiac Arrest Survival: A Time to Act. National Academies Press (US).
[4]
Sarah Masud Preum, Sile Shu, Jonathan Ting, Vincent Lin, Ronald Williams, John Stankovic, and Homa Alemzadeh. 2018. Towards a cognitive assistant system for emergency response. In 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems.
[5]
M Arif Rahman, Sarah Masud Preum, Ronald D Williams, Homa Alemzadeh, and John A Stankovic. 2020. GRACE: Generating Summary Reports Automatically for Cognitive Assistance in Emergency Response. In AAAI. 13356--13362.
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Published In
November 2020
852 pages
ISBN:9781450375900
DOI:10.1145/3384419
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 November 2020
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SenSys '20: The 18th ACM Conference on Embedded Networked Sensor Systems
November 16 - 19, 2020
Virtual Event, Japan
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Overall Acceptance Rate 198 of 990 submissions, 20%
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