Literature DB >> 31866248

Computerized data mining analysis of keywords as indicators of the concepts in AHA-BLS guideline updates.

Hiroshi Sekiguchi1, Tatsuma Fukuda2, Yuichiro Tamaki2, Kazuhiko Hanashiro3, Kenichi Satoh4, Eiichi Ueno5, Ichiro Kukita2.   

Abstract

INTRODUCTION: Cardiopulmonary resuscitation (CPR) guidelines have been updated every 5 years since 2000. Significant changes have been made in each update, and every time a guideline is changed, the instructors of each country that ratify the American Heart Association (AHA) must review the contents of the revised guideline to understand the changes made in the concept of CPR. The purpose of this study was to use a computerized data mining method to identify and characterize the changes in the key concepts of the AHA-Basic Life Support (BLS) updates between 2000 and 2015.
METHODS: We analyzed the guidelines of the AHA-BLS provider manual of 2000, 2005, 2010, and 2015 using a computerized data mining method and attempted to identify the changes in keywords along with changes in the guideline.
RESULTS: In particular, the 2000 guideline has focused on the detailed BLS technique of an individual health care provider, whereas the 2005 and 2010 guidelines have focused on changing the ratio of chest compressions and breathing and changing the BLS sequence, respectively. In the most recent 2015 guideline, the CPR team was the central topic. We observed that as the guidelines were updated over the years, keywords related to CPR and automated external defibrillators (AED) associated with co-occurrence network continued to appear.
CONCLUSIONS: Analysis revealed that keywords related to CPR and AED associated with the co-occurrence network continued to appear. We believe that the results of this study will ultimately contribute to optimizing AHA's educational strategies for health care providers.
Copyright © 2019 Elsevier Inc. All rights reserved.

Keywords:  American Heart Association; Basic Life Support; Cardiopulmonary resuscitation; Data mining analysis; Guideline

Mesh:

Year:  2019        PMID: 31866248     DOI: 10.1016/j.ajem.2019.11.045

Source DB:  PubMed          Journal:  Am J Emerg Med        ISSN: 0735-6757            Impact factor:   2.469


  1 in total

1.  Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things.

Authors:  Zongye Yang
Journal:  PLoS One       Date:  2022-01-26       Impact factor: 3.240

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.