مقاله انگلیسی رایگان در مورد سیستم های تصمیم گیری بالینی برای تریاژ در بخش اورژانس – الزویر ۲۰۲۰

elsevier

 

مشخصات مقاله
ترجمه عنوان مقاله سیستم های تصمیم گیری بالینی برای تریاژ در بخش اورژانس با استفاده از سیستم های هوشمند: مرور
عنوان انگلیسی مقاله Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review
انتشار مقاله سال ۲۰۲۰
تعداد صفحات مقاله انگلیسی ۲۲ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله مروری (Review Article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) Scopus – Master Journals List – JCR – MedLine
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۴٫۴۷۲ در سال ۲۰۱۹
شاخص H_index ۷۴ در سال ۲۰۲۰
شاخص SJR ۱٫۰۲۵ در سال ۲۰۱۹
شناسه ISSN ۰۹۳۳-۳۶۵۷
شاخص Quartile (چارک) Q1 در سال ۲۰۱۹
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر دارد
رفرنس دارد
رشته های مرتبط پزشکی، مهندسی کامپیوتر، مهندسی پزشکی
گرایش های مرتبط طب اورژانس، هوش مصنوعی
نوع ارائه مقاله
ژورنال
مجله  هوش مصنوعی در پزشکی – Artificial Intelligence In Medicine
دانشگاه  Massachusetts Institute of Technology, Massachusetts
کلمات کلیدی تریاژ، CDSS، EHR، یادگیری ماشین، مراقبت های ویژه
کلمات کلیدی انگلیسی Triage, CDSS, EHR, Machine learning, Critical care
شناسه دیجیتال – doi
https://doi.org/10.1016/j.artmed.2019.101762
کد محصول E14568
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract

۱٫ Introduction

۲٫ Methods

۳٫ Results

۴٫ Discussion

۵٫ Conclusions

Acknowledgements

Annexes

References

بخشی از متن مقاله:

Abstract

Motivation: Emergency Departments’ (ED) modern triage systems implemented worldwide are solely based upon medical knowledge and experience. This is a limitation of these systems, since there might be hidden patterns that can be explored in big volumes of clinical historical data. Intelligent techniques can be applied to these data to develop clinical decision support systems (CDSS) thereby providing the health professionals with objective criteria. Therefore, it is of foremost importance to identify what has been hampering the application of such systems for ED triage.

Objectives: The objective of this paper is to assess how intelligent CDSS for triage have been contributing to the improvement of quality of care in the ED as well as to identify the challenges they have been facing regarding implementation.

Methods: We applied a standard scoping review method with the manual search of 6 digital libraries, namely: ScienceDirect, IEEE Xplore, Google Scholar, Springer, MedlinePlus and Web of Knowledge. Search queries were created and customized for each digital library in order to acquire the information. The core search consisted of searching in the papers’ title, abstract and key words for the topics “triage”, “emergency department”/“emergency room” and concepts within the field of intelligent systems.

Results: From the review search, we found that logistic regression was the most frequently used technique for model design and the area under the receiver operating curve (AUC) the most frequently used performance measure. Beside triage priority, the most frequently used variables for modelling were patients’ age, gender, vital signs and chief complaints. The main contributions of the selected papers consisted in the improvement of a patient’s prioritization, prediction of need for critical care, hospital or Intensive Care Unit (ICU) admission, ED Length of Stay (LOS) and mortality from information available at the triage.

Conclusions: In the papers where CDSS were validated in the ED, the authors found that there was an improvement in the health professionals’ decision-making thereby leading to better clinical management and patients’ outcomes. However, we found that more than half of the studies lacked this implementation phase. We concluded that for these studies, it is necessary to validate the CDSS and to define key performance measures in order to demonstrate the extent to which incorporation of CDSS at triage can actually improve care.

Introduction

The growing demand for emergency services, combined with the priority sorting due to patient’s acuity, results in long waiting times for patients. Waiting times have a significant impact on patient mortality, morbidity with readmission in less than 30 days, number of preIntensive Care Units (ICU) resuscitation, length of stay (LOS), patient satisfaction and costs [1–۷]. The outcome of patients’ medical treatment is time-sensitive, therefore the sooner the treatment is rendered, the better the outcome [3–۷].

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