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Register a new userCounterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network
استخراج الحقائق الدعم المضادة لتشخيص السجل الطبي القائم مع شبكة الرسم البياني
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توفير تفسير موثوق للتشخيص السريري بناء على الرقم القياسي الطبي الإلكتروني (EMR) أمر أساسي لتطبيق الذكاء الاصطناعي في المجال الطبي. تعامل الأساليب الحالية في الغالب EMR كأسل تسلسل نصي وتوفير توضيحات بناء على قاعدة معرفة طبية دقيقة، وهي خاصة من الصعب الحصول عليها للخبراء في الواقع. لذلك، نقترح طريقة استخراج حقائق دعم حقائق متعددة التحبيبية متعددة الاستخدامات لاستخراج الحقائق الداعمة من غير النظامية EMR نفسها دون قواعد المعرفة الخارجية في هذه الورقة. على وجه التحديد، ننقل أولا تسلسل EMR إلى شبكة رسم بياني هرمي ثم الحصول على العلاقة السببية بين ميزات وتشخيص متعدد التحبيبية ونتائجها من خلال التدخل العالمي على الرسم البياني. ميزات وجود أقوى اتصال سببي مع النتائج توفر الدعم التفسيري للتشخيص. النتائج التجريبية على EMR الصينية الحقيقية من الوذمة الليمفية توضح أن طريقتنا يمكن أن تشخيص أربعة أنواع من EMR بشكل صحيح، ويمكن أن توفر حقائق داعمة دقيقة للنتائج. والأهم من ذلك، أن النتائج المتعلقة بالأمراض المختلفة توضح متانة نهجنا، والتي تمثل الطلب المحتمل في المجال الطبي.
Providing a reliable explanation for clinical diagnosis based on the Electronic Medical Record (EMR) is fundamental to the application of Artificial Intelligence in the medical field. Current methods mostly treat the EMR as a text sequence and provide explanations based on a precise medical knowledge base, which is disease-specific and difficult to obtain for experts in reality. Therefore, we propose a counterfactual multi-granularity graph supporting facts extraction (CMGE) method to extract supporting facts from irregular EMR itself without external knowledge bases in this paper. Specifically, we first structure the sequence of EMR into a hierarchical graph network and then obtain the causal relationship between multi-granularity features and diagnosis results through counterfactual intervention on the graph. Features having the strongest causal connection with the results provide interpretive support for the diagnosis. Experimental results on real Chinese EMR of the lymphedema demonstrate that our method can diagnose four types of EMR correctly, and can provide accurate supporting facts for the results. More importantly, the results on different diseases demonstrate the robustness of our approach, which represents the potential application in the medical field.
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