أسماء ومعرفات المراقبة المنطقية (LOINC) هي مجموعة قياسية من الرموز التي تمكن الأطباء من التواصل حول الاختبارات الطبية.تعتمد المختبرات على Loinc لتحديد ما تختبر طلبات الطبيب للمريض.ومع ذلك، غالبا ما يستخدم الأطباء رموز مخصصة خاصة بالموقع في أنظمة السجلات الطبية التي يمكن أن تشمل اختلافا بالاختصار والأخطاء الإملائية واخترع المختصرات.يجب أن يتم تعيين حلول البرمجيات من هذه الرموز المخصصة إلى معيار Loinc لدعم قابلية التشغيل البيني للبيانات.التحدي الرئيسي هو أن لوينك تتألف من ستة عناصر.التعيين لا يتطلب عدم استخراج هذه العناصر فحسب، بل يجمع بينها أيضا وفقا لمنطق Loinc.وجدنا أن التعلم العميق القائم على الطابع يتفوق عند استخراج عناصر Loinc بينما تكون الأساليب القائمة على المنطق أكثر فعالية للجمع بين هذه العناصر في قيم Loinc كاملة.في هذه الورقة، نقدم مجموعة من التعلم والمنطق والمنطق المستخدم حاليا في العديد من المرافق الطبية في الخريطة من
Logical Observation Identifiers Names and Codes (LOINC) is a standard set of codes that enable clinicians to communicate about medical tests. Laboratories depend on LOINC to identify what tests a doctor orders for a patient. However, clinicians often use site specific, custom codes in their medical records systems that can include shorthand, spelling mistakes, and invented acronyms. Software solutions must map from these custom codes to the LOINC standard to support data interoperability. A key challenge is that LOINC is comprised of six elements. Mapping requires not only extracting those elements, but also combining them according to LOINC logic. We found that character-based deep learning excels at extracting LOINC elements while logic based methods are more effective for combining those elements into complete LOINC values. In this paper, we present an ensemble of machine learning and logic that is currently used in several medical facilities to map from
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