{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:08:35Z","timestamp":1787026115008,"version":"build-2736575974"},"reference-count":12,"publisher":"Aerospace Medical Association","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:p>\n                    <jats:bold>INTRODUCTION:<\/jats:bold>\n                    This study aimed to update and reinforce previous research on helicopter emergency medical service accidents in the United States. By investigating predictors of fatalities after helicopter emergency medical service crashes through the application of machine learning techniques, we updated existing data sets and sought to uncover patterns that traditional analysis might not reveal.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>METHODS:<\/jats:bold>\n                    Using the National Transportation Safety Board database, the authors analyzed a dataset of 267 helicopter emergency medical service accidents between 1991\u20132022. We first calculated fatalities odds ratios for each condition. We then plotted geospatial locations of all reported accidents. Finally, we used XGBoost regression to understand the most important features contributing to fatality after an accident.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>RESULTS:<\/jats:bold>\n                    The findings reaffirm previous research and identify significant predictors of fatalities in helicopter emergency medical service accidents. Key factors such as adverse flight conditions (weather), the absence of a copilot, and postcrash fires are highlighted as critical to understanding and mitigating risks of fatality.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>DISCUSSION:<\/jats:bold>\n                    These findings emphasize the utility of machine learning in extracting meaningful insights from accident data, suggesting that such techniques offer a more nuanced understanding of the conditions leading to fatalities. It points out the potential of these methods to not only enhance aviation safety but also to be applied across other sectors. We conclude by underlining the significant potential of techniques like XGBoost in advancing safety measures within helicopter emergency medical service and possibly other aviation sectors.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>\n                      Korentsides J, Keebler JR, Berezovski M, Chaparro A.\n                      <jats:italic>Factors contributing to fatalities in helicopter emergency medical service accidents<\/jats:italic>\n                      . Aerosp Med Hum Perform. 2025; 96(2):111\u2013115.\n                    <\/jats:bold>\n                  <\/jats:p>","DOI":"10.3357\/amhp.6461.2025","type":"journal-article","created":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T19:30:46Z","timestamp":1738611046000},"page":"111-115","source":"Crossref","is-referenced-by-count":2,"title":["Factors Contributing to Fatalities in Helicopter Emergency Medical Service Accidents"],"prefix":"10.3357","volume":"96","author":[{"given":"Jenna","family":"Korentsides","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph R.","family":"Keebler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mihhail","family":"Berezovski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex","family":"Chaparro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1911","reference":[{"issue":"7","key":"B1","series-title":"Aerosp Med Hum Perform.","first-page":"563","article-title":"A Bayesian approach on investigating helicopter emergency medical fatal accidents","volume":"92","author":"Simonson RJ","year":"2021","unstructured":"Simonson RJ, Keebler JR, Chaparro A. A Bayesian approach on investigating helicopter emergency medical fatal accidents. Aerosp Med Hum Perform. 2021; 92(7):563\u2013569."},{"key":"B2","first-page":"291","author":"Simonson RJ","year":"2021","unstructured":"Simonson RJ, Keebler JR, Chaparro A. Trends of non-fatal HEMS accident-related injuries. Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care. Los Angeles (CA): SAGE Publications; 2021; 10(1):291\u2013296."},{"key":"B3","first-page":"785","author":"Chen T","year":"2016","unstructured":"Chen T, Guestrin C. XGBoost: a scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; August 13-17, 2016; San Francisco, CA. New York (NY): Association for Computing Machinery; 2016:785\u2013794."},{"key":"B4","author":"Mehta J","year":"2021","unstructured":"Mehta J, Vatsaraj V, Shah J, Godbole A. Airplane crash severity prediction using machine learning. 12th International Conference on Computing Communication and Networking Technologies (ICCCNT); July 6-8, 2021; Kharagpur, India. Piscataway (NJ): Institute of Electrical and Electronics Engineers; 2021:1\u20136."},{"issue":"4","key":"B5","series-title":"Ann Emerg Med.","first-page":"351","article-title":"EMS helicopter crashes: what influences fatal outcome?","volume":"47","author":"Baker SP","year":"2006","unstructured":"Baker SP, Grabowski JG, Dodd RS, Shanahan DF, Lamb MW, Li GH. EMS helicopter crashes: what influences fatal outcome? Ann Emerg Med. 2006; 47(4):351\u2013356."},{"key":"B6","series-title":"Decision making in aviation.","first-page":"271","article-title":"Automation bias: decision making and performance in high-tech cockpits","author":"Mosier KL","year":"2017","unstructured":"Mosier KL, Skitka LJ, Heers S, Burdick M. Automation bias: decision making and performance in high-tech cockpits. In: Harris D, Li WC, editors. Decision making in aviation. Oxfordshire (UK): Routledge; 2017:271\u2013288."},{"issue":"1","key":"B7","series-title":"Aerosp Med Hum Perform.","first-page":"26","article-title":"Occupant injury severity and accident causes in helicopter emergency medical services (1983\u20132014)","volume":"87","author":"Boyd DD","year":"2016","unstructured":"Boyd DD, Macchiarella ND. Occupant injury severity and accident causes in helicopter emergency medical services (1983\u20132014). Aerosp Med Hum Perform. 2016; 87(1):26\u201331."},{"issue":"4","key":"B8","series-title":"Aerosp Med Hum Perform.","first-page":"396","article-title":"Systems safety risk analysis of fatal night helicopter emergency medical service accidents","volume":"90","author":"Aherne BB","year":"2019","unstructured":"Aherne BB, Zhang C, Chen WS, Newman DG. Systems safety risk analysis of fatal night helicopter emergency medical service accidents. Aerosp Med Hum Perform. 2019; 90(4):396\u2013404."},{"issue":"6","key":"B9","series-title":"Aerosp Med Hum Perform.","first-page":"550","article-title":"Pilot domain task experience in night fatal helicopter emergency medical service accidents","volume":"87","author":"Aherne BB","year":"2016","unstructured":"Aherne BB, Zhang C, Newman DG. Pilot domain task experience in night fatal helicopter emergency medical service accidents. Aerosp Med Hum Perform. 2016; 87(6):550\u2013556."},{"key":"B10","unstructured":"National Transportation Safety Board. NTSB monthly aviation accident lists, and dashboard of aviation accidents by state or region. [Accessed December 11, 2024]. Available from https:\/\/www.ntsb.gov\/Pages\/monthly.aspx."},{"key":"B11","first-page":"2347","author":"Eckholm WA","year":"2002","unstructured":"Eckholm WA, Bennett JM. Advances in fire protection for critical vehicle components. Proceedings of the 2002 SAE Motorsports Engineering Conference and Exhibition. December 2-5, 2002; Indianapolis, IN. Warrendale (PA): SAE International; 2002:2347\u20132359. SAE Technical Paper Series, 2002-01-3325."},{"issue":"5","key":"B12","series-title":"Br J Anaesth.","first-page":"796","article-title":"Lessons from aviation safety: pilot monitoring, the sterile flight deck rule, and aviation-style computerised checklists in the operating room","volume":"131","author":"Jelacic S","year":"2023","unstructured":"Jelacic S, Bowdle A, Nair BG, Nair AA, Edwards M, Boorman DJ. Lessons from aviation safety: pilot monitoring, the sterile flight deck rule, and aviation-style computerised checklists in the operating room. 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