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Register a new userModeling Diagnostic Label Correlation for Automatic ICD Coding
نمذجة العلاقة التسمية التشخيصية لترميز ICD التلقائي
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بالنظر إلى الملاحظات السريرية المكتوبة في السجلات الصحية الإلكترونية (EHRS)، فمن الصعب التنبؤ برموز التشخيص التي يتم صياغة مهمة تصنيف متعدد العلامات.مجموعة كبيرة من الملصقات، والاعتماد الهرمي، والبيانات غير المتوازنة تجعل مهمة التنبؤ هذه صعبة للغاية.قامت معظم الأعمال الحالية ببناء التنبؤ الثنائي لكل تسمية بشكل مستقل، مما يتجاهل التبعيات بين الملصقات.لمعالجة هذه المشكلة، نقترح إطارا من مرحلتين لتحسين ترميز ICD التلقائي عن طريق التقاط ارتباط التسمية.على وجه التحديد، ندرب مقدر توزيع مجموعة التسمية لإنقاش احتمالية كل مرشح تعيين كل ملصق تم إنشاؤه بواسطة مؤشر أساسي.هذه الورقة هي المحاولة الأولى لتعلم توزيع مجموعة التسمية كوحدة Reranking لترميز ICD.في التجارب، يكون إطار عملنا المقترح قادرا على تحسين تنبؤات أفضل أداء لتنبؤ الكود الطبي في مجموعات البيانات المقلدة القياسية.
Given the clinical notes written in electronic health records (EHRs), it is challenging to predict the diagnostic codes which is formulated as a multi-label classification task. The large set of labels, the hierarchical dependency, and the imbalanced data make this prediction task extremely hard. Most existing work built a binary prediction for each label independently, ignoring the dependencies between labels. To address this problem, we propose a two-stage framework to improve automatic ICD coding by capturing the label correlation. Specifically, we train a label set distribution estimator to rescore the probability of each label set candidate generated by a base predictor. This paper is the first attempt at learning the label set distribution as a reranking module for ICD coding. In the experiments, our proposed framework is able to improve upon best-performing predictors for medical code prediction on the benchmark MIMIC datasets.
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