{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:42:00Z","timestamp":1760488920764,"version":"3.41.0"},"reference-count":21,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2019,8,16]],"date-time":"2019-08-16T00:00:00Z","timestamp":1565913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGBED Rev."],"published-print":{"date-parts":[[2019,8,16]]},"abstract":"<jats:p>\n            This paper presents our preliminary results on development of a Cognitive assistant system for\n            <jats:italic>Emergency Medical Services (CognitiveEMS)<\/jats:italic>\n            that aims to improve situational awareness and safety of first responders.\n            <jats:italic>CognitiveEMS<\/jats:italic>\n            integrates a suite of smart wearable sensors, devices, and analytics for real-time collection and analysis of in-situ data from incident scene and delivering dynamic data-driven insights to responders on the most effective response actions to take. We present the overall architecture of\n            <jats:italic>CognitiveEMS<\/jats:italic>\n            pipeline for processing information collected from the responder, which includes stages for converting speech to text, extracting medical and EMS protocol specific concepts, and modeling and execution of an EMS protocol. The performance of the pipeline is evaluated in both noise-free and noisy incident environments. The experiments are conducted using two types of publicly-available real EMS data: short radio calls and post-incident patient care reports. Three different noise profiles are considered for simulating the noisy environments: cafeteria, people talking, and emergency sirens. Noise was artificially added at 3 intensity levels of low, medium, and high to pre-recorded audio data. The results show that the i) state-of-the-art speech recognition tools such as Google Speech API are quite robust to low and medium noise intensities; ii) in the presence of high noise levels, the overall recall rate in medical concept annotation is reduced; and iii) the effect of noise often propagates to the final decision making stage and results in generating misleading feedback to responders.\n          <\/jats:p>","DOI":"10.1145\/3357495.3357502","type":"journal-article","created":{"date-parts":[[2019,8,16]],"date-time":"2019-08-16T19:35:19Z","timestamp":1565984119000},"page":"51-60","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["CognitiveEMS"],"prefix":"10.1145","volume":"16","author":[{"given":"Sarah","family":"Preum","sequence":"first","affiliation":[{"name":"University of Virginia"}]},{"given":"Sile","family":"Shu","sequence":"additional","affiliation":[{"name":"University of Virginia"}]},{"given":"Mustafa","family":"Hotaki","sequence":"additional","affiliation":[{"name":"University of Virginia"}]},{"given":"Ronald","family":"Williams","sequence":"additional","affiliation":[{"name":"University of Virginia"}]},{"given":"John","family":"Stankovic","sequence":"additional","affiliation":[{"name":"University of Virginia"}]},{"given":"Homa","family":"Alemzadeh","sequence":"additional","affiliation":[{"name":"University of Virginia"}]}],"member":"320","published-online":{"date-parts":[[2019,8,16]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"{n. d.}. pyDatalog. https:\/\/pypi.python.org\/pypi\/pyDatalog.  {n. d.}. pyDatalog. https:\/\/pypi.python.org\/pypi\/pyDatalog."},{"key":"e_1_2_1_2_1","unstructured":"{n. d.}. UMLS Metathreasurus API. \"https:\/\/www.nlm.nih.gov\/research\/umls\/knowledge_sources\/metathesaurus\/\".  {n. d.}. UMLS Metathreasurus API. \"https:\/\/www.nlm.nih.gov\/research\/umls\/knowledge_sources\/metathesaurus\/\"."},{"key":"e_1_2_1_3_1","unstructured":"1998. LabVIEW User Manual. National Instruments Austin TX (1998).  1998. LabVIEW User Manual. National Instruments Austin TX (1998)."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1136\/jamia.2009.002733"},{"key":"e_1_2_1_5_1","unstructured":"Visible Body. 2018. Access a wealth of critical emergency reference information and helpful tools. https:\/\/www.visiblebody.com\/anatomy-and-physiology-apps\/informeds-emergency-critical-care-guide. {Online; accessed 10-Jan-2018}.  Visible Body. 2018. Access a wealth of critical emergency reference information and helpful tools. https:\/\/www.visiblebody.com\/anatomy-and-physiology-apps\/informeds-emergency-critical-care-guide. {Online; accessed 10-Jan-2018}."},{"key":"e_1_2_1_6_1","unstructured":"Nancy L Caroline. 2007. Nancy Caroline's emergency care in the streets. Vol. 2. Jones & Bartlett Learning.  Nancy L Caroline. 2007. Nancy Caroline's emergency care in the streets . Vol. 2. Jones & Bartlett Learning."},{"key":"e_1_2_1_7_1","unstructured":"2017 Thomas Jefferson EMS Council. 2018. Thomas Jefferson EMS Council app. https:\/\/itunes.apple.com\/us\/app\/tjems\/id594746889?mt=8. {Online; accessed 1-Feb-2018}.  2017 Thomas Jefferson EMS Council. 2018. Thomas Jefferson EMS Council app. https:\/\/itunes.apple.com\/us\/app\/tjems\/id594746889?mt=8. {Online; accessed 1-Feb-2018}."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/SIEDS.2016.7489306"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2594368.2594383"},{"volume-title":"ASR2000-Automatic Speech Recognition: Challenges for the new Millenium ISCA Tutorial and Research Workshop (ITRW).","year":"2000","author":"Hirsch Hans-G\u00fcnter","key":"e_1_2_1_10_1"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2006.1659988"},{"key":"e_1_2_1_12_1","unstructured":"ImageTrend. 2017. Critical Care Solutions ImageTrend. http:\/\/www.imagetrend.com\/solutions-ems-critical-care\/. {Online; accessed 10-Jan-2018}.  ImageTrend. 2017. Critical Care Solutions ImageTrend. http:\/\/www.imagetrend.com\/solutions-ems-critical-care\/. {Online; accessed 10-Jan-2018}."},{"key":"e_1_2_1_13_1","first-page":"20","article-title":"Comparing speech recognition systems (Microsoft API, Google API and CMU Sphinx)","volume":"7","author":"K\u00ebpuska Veton","year":"2017","journal-title":"Int. J. Eng. Res. Appl"},{"key":"e_1_2_1_14_1","volume-title":"Informatics","volume":"4","author":"Betty Khong Peck Chui","year":"2017"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P14-5010"},{"volume-title":"Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems-Volume 3. 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Studies in health technology and informatics 245 (2017) 1153--1157."},{"key":"e_1_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Ergin Soysal Jingqi Wang Min Jiang Yonghui Wu Serguei Pakhomov Hongfang Liu and Hua Xu. 2017. CLAMP-a toolkit for efficiently building customized clinical natural language processing pipelines. Journal of the American Medical Informatics Association (2017).  Ergin Soysal Jingqi Wang Min Jiang Yonghui Wu Serguei Pakhomov Hongfang Liu and Hua Xu. 2017. CLAMP-a toolkit for efficiently building customized clinical natural language processing pipelines. Journal of the American Medical Informatics Association (2017).","DOI":"10.1093\/jamia\/ocx132"},{"key":"e_1_2_1_21_1","unstructured":"Willie Walker Paul Lamere Philip Kwok Bhiksha Raj Rita Singh Evandro Gouvea Peter Wolf and Joe Woelfel. 2004. Sphinx-4: A flexible open source framework for speech recognition. (2004).   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(2004)."}],"container-title":["ACM SIGBED Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3357495.3357502","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3357495.3357502","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:23:22Z","timestamp":1750202602000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3357495.3357502"}},"subtitle":["a cognitive assistant system for emergency medical services"],"short-title":[],"issued":{"date-parts":[[2019,8,16]]},"references-count":21,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,8,16]]}},"alternative-id":["10.1145\/3357495.3357502"],"URL":"https:\/\/doi.org\/10.1145\/3357495.3357502","relation":{},"ISSN":["1551-3688"],"issn-type":[{"type":"electronic","value":"1551-3688"}],"subject":[],"published":{"date-parts":[[2019,8,16]]},"assertion":[{"value":"2019-08-16","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}