Towards a Cognitive Assistant System for Emergency Response

Preum, Sarah Masud; Shu, Sile; Ting, Jonathan; Lin, Vincent; Williams, Ronald D.; Stankovic, John A.; Alemzadeh, Homa

doi:10.1109/iccps.2018.00047

Cited by 14 publications

(3 citation statements)

References 2 publications

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“…The first step of our solution is speech-to-text conversion, marked by 1 in figure 1. There is a lot of noise in EMS scenes, and the accuracy of transcriptions are significantly affected under such adverse conditions (Preum et al 2018). Solving this problem is important, but not the subject of this paper.…”

Section: Speech-to-text Conversionmentioning

confidence: 98%

“…But this system is deployed only in the trauma domain, GRACE is more generic and can be used for any medical emergency scenario, if the documentation format is similar. Preum et al in (Preum et al 2018), Shu et al in (Shu et al 2019) and Lindes et al in (Lindes, Lonsdale, and Embley 2015) discuss the idea of developing cognitive assistant systems to improve and aid the awareness of first-responders. However, they do not focus on EMS incident report generation or documentation for the patients involved.…”

Section: Related Workmentioning

confidence: 99%

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GRACE: Generating Summary Reports Automatically for Cognitive Assistance in Emergency Response

Rahman

Preum

Williams

et al. 2020

AAAI

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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.

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Section: Speech-to-text Conversionmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

GRACE: Generating Summary Reports Automatically for Cognitive Assistance in Emergency Response

Rahman

Preum

Williams

et al. 2020

AAAI

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“…The innovation takes voice information from people on call in a loud climate and investigations it to extricate significant data for independent direction. The entire information handling pipeline comprises of discourse to-message change, data extraction (named element acknowledgment, transient articulation extraction, and clinical idea extraction), and convention demonstrating and execution [12].…”

Section: Improvement In Response For Power Emergency Events and Disas...mentioning

confidence: 99%

A systematic review of data-driven & machine learning frameworks for minimizing the emergency response rate

Aftab

2023

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Many blackouts have occurred in recent years across the world, wreaking havoc on socioeconomic progress. As a result, it has become a crucial area for research into emergency scenarios like power outages, traffic management, and petrochemical unit dangers, as well as ways for decreasing losses caused by these events. Because the most essential item in an endangered circumstance is life, a person will discover a rapid and precise solution with little response time in an uncommon situation. Many lives have been lost in recent years as a result of ineffective emergency response. Therefore, the main goal of the research is to develop a data-driven emergency response system based on efficient machine learning techniques that is independent of human resources and will provide the necessary emergency response in a fast way. This paper offers preliminary findings from the development of the Emergency Response Assist System, which intends to increase first respond situational awareness and safety. The system collects the essential information from text format about what the caller will say, systematically produces cases, determines the type of the case, and then informs the appropriate department. It keeps track of response time since computers are significantly faster and more efficient than people. Experiments on real crash data and models using data sets show a significant reduction in resource requirements and an accurate reduction in emergency response time.

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The Technique of Data Analysis Tasks Distribution in the Fog-Computing Environment

Melnik

Klimenko

et al. 2020

Advances in Intelligent Systems and Computing

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Towards a Cognitive Assistant System for Emergency Response

Cited by 14 publications

References 2 publications

GRACE: Generating Summary Reports Automatically for Cognitive Assistance in Emergency Response

GRACE: Generating Summary Reports Automatically for Cognitive Assistance in Emergency Response

A systematic review of data-driven & machine learning frameworks for minimizing the emergency response rate

The Technique of Data Analysis Tasks Distribution in the Fog-Computing Environment

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