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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

Published:16 November 2020Publication History

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.Google ScholarGoogle ScholarCross RefCross Ref
  2. Google. [n.d.]. Speech-to-Text API by Google.. In https://groups.google.com/forum/#!topic/nltk-users/CS2fCFxvu1I.Google ScholarGoogle Scholar
  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).Google ScholarGoogle Scholar
  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.Google ScholarGoogle ScholarDigital LibraryDigital Library
  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.Google ScholarGoogle Scholar

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  1. 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

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        • Published in

          cover image ACM Conferences
          SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems
          November 2020
          852 pages
          ISBN:9781450375900
          DOI:10.1145/3384419

          Copyright © 2020 ACM

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          New York, NY, United States

          Publication History

          • Published: 16 November 2020

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