چکیده مقاله
The healthcare industry generates vast amounts of textual data in the form of medical records, prescriptions, and insurance claims This paper introduces PERSiMED PERSian Intelligent MEDical document reader , a novel deep learning based framework designed to automate the extraction, processing, and semantic analysis of Persian medical documents The proposed system employs a sophisticated pipeline combining Convolutional Neural Networks CNN for document preprocessing and Vision Language VL models for intelligent content extraction PERSiMED addresses critical pain points in the health insurance industry, including fraud detection, claims processing automation, privacy preserving data management, and longitudinal health record creation Our framework achieves end to end automation from image input through auto rotation, orientation correction, boundary detection, to semantic annotation and structured HTML output, enabling seamless database integration This research demonstrates how modern AI techniques can be effectively adapted to handle the unique challenges of Mixed English and Persian script medical documentation
کلیدواژهها
نویسندگان
Seyed Mohammad Mahdi Khadem
Jamil Seydi Moradi
Jamil Seydi Moradi
Seyed Mohammad Mahdi Khadem
شیوه ارجاع
Khadem, Seyed Mohammad Mahdi and Seydi Moradi, Jamil,1404,PERSiMED: PERSian Intelligent MEDical Document Reader:A Smart NLP-based Framework for Persian Medical Documents,The 32nd National Conference and the 13th International Conference on Insurance and Development: 'Reimagining Supervision in Insurance: An Innovative Path toward Effective Regulation',Tehran
ارائهشده در
مجموعه مقالات سی ودومین همایش ملی و سیزدهمین همایش بین المللی بیمه و توسعه با عنوان: «بازاندیشی نظارت در بیمه؛ مسیر نوآورانه برای تنظیم گری موثر»11 آذر 1404 · تهران