Bridging classification for injury diagnoses that can be converted to both the <scp>I</scp>nternational <scp>C</scp>lassification of <scp>D</scp>iseases and the <scp>A</scp>bbreviated <scp>I</scp>njury <scp>S</scp>cale

Nakahara, Shinji; Uchida, Yuzo; Oda, Jun; Yokota, Junichiro

doi:10.1002/ams2.2

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…The injuries were divided into nine body regions; head, neck, face, thorax, abdomen, spine, upper extremities, lower extremities (including pelvic fractures) and external in accordance with the Abbreviate Injury Score (AIS) [ 9 ]. Injuries were scored as isolated injury or multiple injuries in case of polytrauma.…”

Section: Methodsmentioning

confidence: 99%

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Trauma care during the COVID-19 pandemic in the Netherlands: a level 1 trauma multicenter cohort study

Hakkenbrak

Loggers

Lubbers

et al. 2021

Scand J Trauma Resusc Emerg Med

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Purpose The coronavirus (COVID-19) pandemic has caused major healthcare challenges worldwide resulting in an exponential increase in the need for hospital- and intensive care support for COVID-19 patients. As a result, surgical care was restricted to urgent cases of surgery. However, the care for trauma patients is not suitable for reduction or delayed treatment. The influence of the pandemic on the burden of disease of trauma care remains to be elucidated. Methods All patients with traumatic injuries that were presented to the emergency departments (ED) of the Amsterdam University Medical Center, Location Academic Medical Center (AMC) and VU medical center (VUMC) and the Northwest Clinics (NWC) between March 10, 2019 and May 10, 2019 (non-COVID) and March 10, 2020 and May 10, 2020 (COVID-19 period) were included. The primary outcome was the difference in ED admissions for trauma patients between the non-COVID and COVID-19 study period. Additionally, patient- and injury characteristics, health care consumption, and 30-day mortality were evaluated. Results A 37% reduction of ED admissions for trauma patients was seen during the COVID-19 pandemic (non-COVID n = 2423 and COVID cohort n = 1531). Hospital admission was reduced by 1.6 trauma patients per day. Fewer patients sustained car- and sports-related injuries. Injuries after high energetic trauma were more severe in the COVID-19 period (Injury Severity Score 17.3 vs. 12.0, p = 0.006). Relatively more patients were treated operatively (21.4% vs. 16.6%, p < 0.001) during the COVID-19 period. Upper-(17.6 vs. 12.5%, p = 0.002) and lower extremity injuries (30.7 vs. 23.0%, p = 0.002) mainly accounted for this difference. The 30-day mortality rate was higher during the pandemic (1.0 vs. 2.3%, p = 0.001). Conclusion The burden of disease and healthcare consumption of trauma patients remained high during the COVID-19 pandemic. Results of this study can be used to optimize the use of hospital capacity and anticipate health care planning in future outbreaks.

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Section: Methodsmentioning

confidence: 99%

“…In case of multiple injuries, the AIS was used to score the severity of the injured body regions ranging from 1 (minor injury e.g., contusion or simple fracture) up to 6 (untreatable injuries e.g., spinal cord transection above the level of C3, traumatic brain injury including brain stem laceration or massive bleeding) [ 9 , 10 ].…”

Section: Methodsmentioning

confidence: 99%

Trauma care during the COVID-19 pandemic in the Netherlands: a level 1 trauma multicenter cohort study

Hakkenbrak

Loggers

Lubbers

et al. 2021

Scand J Trauma Resusc Emerg Med

View full text Add to dashboard Cite

show abstract

“…In addition, ICD coding is known to be complex and difficult, and the results obtained from it vary greatly depending on the skill level and proficiency of each in-dividual coder [7]. Computers and automation can enable and inspire new ways of working [8], and computer-assisted systems can significantly improve the efficiency of ICD coding and reduce labor waste [9].…”

Section: Introductionmentioning

confidence: 99%

Automated Generation of ICD-11 Cluster Codes for Precision Medical Record Classification

Feng,

Zhang,

Chen

et al. 2024

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Accurate clinical coding using the International Classification of Diseases (ICD) standard is essential for healthcare analytics. ICD-11 introduces new coding guidelines and cluster structures, posing challenges for existing coding tools. This research presents an automated approach to generate valid ICD-11 cluster codes from medical text. Natural language records are represented as vectors and compared to an ICD-11 corpus using cosine similarity. A bidirectional matching technique then refines similarity estimation. Experiments demonstrate the method yields up to 0.91 F1 score in coding accuracy, significantly outperforming a baseline tool. This work enables efficient high-quality ICD-11 coding to support healthcare informatics.

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Effect of Reclined and Rotated Seating for Automated Driving Systems

Reichert¹,

Kan²

2022

SAE Technical Paper Series

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Bridging classification for injury diagnoses that can be converted to both the International Classification of Diseases and the Abbreviated Injury Scale

Cited by 3 publications

References 9 publications

Trauma care during the COVID-19 pandemic in the Netherlands: a level 1 trauma multicenter cohort study

Trauma care during the COVID-19 pandemic in the Netherlands: a level 1 trauma multicenter cohort study

Automated Generation of ICD-11 Cluster Codes for Precision Medical Record Classification

Effect of Reclined and Rotated Seating for Automated Driving Systems

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