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Register a new userMulti-label Diagnosis Classification of Swedish Discharge Summaries -- ICD-10 Code Assignment Using KB-BERT
تصنيف التشخيص متعدد التسميات الملخصات السويدية للتصوير - ICD-10 تعيين رمز باستخدام KB-Bert
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التصنيف الدولي للأمراض (ICD) هو نظام لتسجيل تشخيصات المرضى بشكل منهجي. تخصص الأطباء أو المبرمون المحترفون رموز ICD للسجلات الطبية للمرضى لتسهيل التمويل والبحث والإدارة. في معظم المرافق الصحية، الترميز السريرية هي مهمة يدوية مطالبة الوقت غير عرضة للأخطاء. أداة تقوم تلقائيا بتعيين رموز ICD إلى النص السريري المجاني يمكن أن توفر الوقت وتقليل الترميز الخاطئ. في حين ركزت العديد من الدراسات السابقة على ترميز ICD، فإن البحث عن سجلات المرضى السويدية نادرة. استكشفت هذه الدراسة أساليب مختلفة لزيادة الملاحظات السريرية السويدية برموز ICD. تم مقارنة KB-Bert، نموذج Bert المدرب مسبقا على النص السويدي، مع نماذج التعلم التقليدية التي تدعمها تدعم أجهزة Vector، وأشجار القرار، وتستخدم جيران K-Learpors كأساس. عند النظر في رموز ICD التي تم تجمعها إلى عشرة كتل، كانت KB-Bert متفوقة على النماذج الأساسية، والحصول على F1-Micro من 0.80 وما ماكرو F1 من 0.58. عند النظر في رموز ICD الكاملة البالغ عددها 263، تفوقت KB-Bert على جميع النماذج الأساسية في F1-Micro و F1-Macro من الصفر. أظهرت اختبارات Wilcoxon الموقعة المرتبة أن اختلافات الأداء بين BERT KB-Bert ونماذج الأساس كانت ذات دلالة إحصائية.
The International Classification of Diseases (ICD) is a system for systematically recording patients' diagnoses. Clinicians or professional coders assign ICD codes to patients' medical records to facilitate funding, research, and administration. In most health facilities, clinical coding is a manual, time-demanding task that is prone to errors. A tool that automatically assigns ICD codes to free-text clinical notes could save time and reduce erroneous coding. While many previous studies have focused on ICD coding, research on Swedish patient records is scarce. This study explored different approaches to pairing Swedish clinical notes with ICD codes. KB-BERT, a BERT model pre-trained on Swedish text, was compared to the traditional supervised learning models Support Vector Machines, Decision Trees, and K-nearest Neighbours used as the baseline. When considering ICD codes grouped into ten blocks, the KB-BERT was superior to the baseline models, obtaining an F1-micro of 0.80 and an F1-macro of 0.58. When considering the 263 full ICD codes, the KB-BERT was outperformed by all baseline models at an F1-micro and F1-macro of zero. Wilcoxon signed-rank tests showed that the performance differences between the KB-BERT and the baseline models were statistically significant.
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The task of automatic diagnosis encoding into standard medical classifications and ontologies, is of great importance in medicine - both to support the daily tasks of physicians in the preparation and reporting of clinical documentation, and for auto
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