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كيفية الاستفادة من بيانات EHR متعددة الوسائط لتحسين التنبؤ الطبي؟
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أصبحت الرعاية الصحية موضوع بحث أكثر وأكثر أهمية مؤخرا. مع البيانات المتنامية في مجال الرعاية الصحية، فإنه يوفر فرصة رائعة للتعلم العميق لتحسين جودة الخدمة وتقليل التكاليف. ومع ذلك، فإن تعقيد بيانات السجلات الصحية الإلكترونية (EHR) هي تحديا لتطبيق التعلم العميق. على وجه التحديد، تتم مراقبة البيانات التي تم إنتاجها في القبول في المستشفى من قبل نظام EHR، والذي يتضمن بيانات منظمة مثل درجة حرارة الجسم اليومية والبيانات غير المنظمة مثل النصوص المجانية والقياسات المختبرية. على الرغم من وجود بعض الأطر المعالجة المسبقة المقترحة لبيانات EHR المحددة، فإن الملاحظات السريرية التي تحتوي على قيمة سريرية كبيرة تتجاوز عالم نظرها. بالإضافة إلى ذلك، سواء كانت هذه البيانات المختلفة من وجهات النظر المختلفة هي مفيدة لجميع المهام الطبية وكيفية الاستفادة من أفضل هذه البيانات لا تزال غير واضحة. لذلك، في هذه الورقة، نقوم أولا باستخراج الملاحظات السريرية المصاحبة من EHR وتقترح طريقة لدمج هذه البيانات، كما ندرس بشكل شامل النماذج المختلفة وأساليب نفاد البيانات لتحسين أداء تنبؤ المهام الطبي بشكل أفضل. تظهر النتائج على مهام التنبؤتين أن نموذجنا المنصوص عليه مع بيانات مختلفة تتفوق على الطريقة التي من أحدثها دون ملاحظات سريرية، توضح أهمية طريقة الانصهار وميزات الملاحظات السريرية.
Healthcare is becoming a more and more important research topic recently. With the growing data in the healthcare domain, it offers a great opportunity for deep learning to improve the quality of service and reduce costs. However, the complexity of electronic health records (EHR) data is a challenge for the application of deep learning. Specifically, the data produced in the hospital admissions are monitored by the EHR system, which includes structured data like daily body temperature and unstructured data like free text and laboratory measurements. Although there are some preprocessing frameworks proposed for specific EHR data, the clinical notes that contain significant clinical value are beyond the realm of their consideration. Besides, whether these different data from various views are all beneficial to the medical tasks and how to best utilize these data remain unclear. Therefore, in this paper, we first extract the accompanying clinical notes from EHR and propose a method to integrate these data, we also comprehensively study the different models and the data leverage methods for better medical task prediction performance. The results on two prediction tasks show that our fused model with different data outperforms the state-of-the-art method without clinical notes, which illustrates the importance of our fusion method and the clinical note features.
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