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Register a new userAutomatic Transformation of Clinical Narratives into Structured Format
التحول التلقائي للروايات السريرية إلى تنسيق منظم
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تتوفر كميات هائلة من البيانات في مجال الرعاية الصحية بتنسيق نص غير منظم، عادة باللغة المحلية للبلدان. تحتوي هذه المستندات على معلومات قيمة. الاستخدام الثانوي للسرد السريري واستخراج المعلومات من الحقائق والعلاقات الرئيسية منها حول تاريخ مرض المريض يمكن أن تعزز الطب الوقائي وتحسين الرعاية الصحية. في هذه الورقة، نقترح طريقة هجينة للتحول التلقائي للنص السريري إلى تنسيق منظم. يتم التقسيم الوثائق تلقائيا في الأجزاء التالية: التشخيص، تاريخ المريض، حالة المريض، نتائج المختبرات. بالنسبة لقسم التشخيص "يتم تطبيق ترميز التعلم العميق النصي في رموز ICD-10 باستخدام MBG-Clinicalbert - نموذج Clinicalbert المصنف من الناعم للنص الطبي البلغاري. من قسم "تاريخ المريض"، نحدد أعراض المريض باستخدام نهج يستند إلى القواعد معززة مع بحث التشابه بناء على Adgeddings Word MBG-Clinicalbert. ونحن نحدد أيضا علاقات الأعراض مثل النفي. بالنسبة لوصف حالة المريض "" وصف، يتم استخدام التصنيف الثنائي لتحديد حالة كل عضو تشريحي. في هذه الورقة، نوضح طرق مختلفة لتكييف أدوات NLP للغة الإنجليزية واللغات الأخرى لغات موارد منخفضة مثل البلغارية.
Vast amounts of data in healthcare are available in unstructured text format, usually in the local language of the countries. These documents contain valuable information. Secondary use of clinical narratives and information extraction of key facts and relations from them about the patient disease history can foster preventive medicine and improve healthcare. In this paper, we propose a hybrid method for the automatic transformation of clinical text into a structured format. The documents are automatically sectioned into the following parts: diagnosis, patient history, patient status, lab results. For the Diagnosis'' section a deep learning text-based encoding into ICD-10 codes is applied using MBG-ClinicalBERT - a fine-tuned ClinicalBERT model for Bulgarian medical text. From the Patient History'' section, we identify patient symptoms using a rule-based approach enhanced with similarity search based on MBG-ClinicalBERT word embeddings. We also identify symptom relations like negation. For the Patient Status'' description, binary classification is used to determine the status of each anatomic organ. In this paper, we demonstrate different methods for adapting NLP tools for English and other languages to a low resource language like Bulgarian.
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