کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
554639 1451060 2016 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A case-based reasoning system for aiding detection and classification of nosocomial infections
ترجمه فارسی عنوان
سیستم استدلال مبتنی بر مورد برای کمک به تشخیص و طبقه بندی عفونت‌های بیمارستانی
کلمات کلیدی
عفونت های بهداشت و درمان در ارتباط . نظارت خودکار. تشخیص عفونت و طبقه بندی؛ استدلال مبتنی بر مورد ؛ سیستم پشتیبانی تصمیم گیری بالینی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر سیستم های اطلاعاتی
چکیده انگلیسی


• Automatic surveillance of healthcare-associated infections.
• Diagnostic decision support system aiding monitoring and control.
• Case-based reasoning system for classifying nosocomial infections.
• Static rule-based knowledge representation and dynamic induction process.
• Natural language processing for physician narratives and nurses' comments.

Nowadays, it is recognized worldwide that healthcare-associated infections are responsible for an increase in patient morbidity, mortality, and higher costs related to prolonged hospital stays. As electronic health data are increasingly available today, there is a unique opportunity to implement real-time decision support systems for automating the surveillance of healthcare-associated infections. As a consequence, different electronic surveillance systems have been implemented to date with varying degrees of success. However, there have been few instances in which clinical data and physician narratives with the potential to significantly improve electronic surveillance alternatives have been adopted. In this context, the present work introduces a case-based reasoning system for the automatic surveillance and diagnosis of healthcare-associated infections. The developed system makes use of different machine learning techniques in order to (i) automatically extract evidence from different types of data including clinical unstructured documents, (ii) incorporate static a priori knowledge handled by infection preventionists, and (iii) dynamically generate new knowledge as well as understandable explanations about the system's decisions. Results obtained from a real deployment in a public hospital belonging to the Spanish National Health System trained with 2569 samples belonging to 1800 patients during more than 10 consecutive months recognize the usefulness of the system.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Decision Support Systems - Volume 84, April 2016, Pages 104–116
نویسندگان
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