Medical Coding between Medical Records Translation and Transcription A Digital Discourse Analysis of the Procedures of Healthcare Service Provider | ||
| مجلة کلية الاداب.جامعة المنصورة | ||
| Volume 77, Issue 77, August 2025 | ||
| Document Type: العلوم الانسانیة الأدبیة واللغات | ||
| DOI: 10.21608/artman.2025.371475.3020 | ||
| Author | ||
| بريك عبدالله الدوشان* | ||
| جامعة المنصورة/ كلية الاداب/ لغة انجليزي | ||
| Abstract | ||
| coding is the process of converting information from patient medical records into standardized predefined medical codes. Numerous globally recognized medical coding schemes pertain to diagnoses and medical procedures; however, in Saudi Arabia, the ICD-10 Australian Modification (ICD-10-AM) serves as the standard for classifying clinical records. I work as a medical coder in Saudi Arabia and was part of the first cohort trained in medical coding in the country, with the training beginning in 2019 and graduation in 2020. After passing the classification exam administered by the Saudi Commission for Health Specialties, I commenced my career as a medical coder at Al-Khafji General Hospital in the Eastern Province, Saudi Arabia. At that time, there were no medical coders in the hospital, and we initiated the implementation of ICD-10-AM coding. Accurate medical coding is essential as it is utilized by hospitals for insurance billing. Following discharge, a patient may be allocated multiple ICD-10-AM codes, making the coding problem a multi-label classification challenge. This study presents a multi-label large-margin classifier that autonomously learns the intrinsic inter-code structure and facilitates the deliberate integration of prior knowledge regarding medical code relationships. Besides enhancing and comprehending the code relationships, the proposed classifier may also leverage this shared information to improve classification performance. Experiments conducted on a publicly accessible dataset of clinical free text and corresponding medical codes demonstrated that the proposed multi-label large-margin classifier outperforms comparable multi-label models in this context. | ||
| Keywords | ||
| Medical data mining; multi-label classification; medical coding; ICD; classificati | ||
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