Pseudo-Relevance Feedback with Deep Learning for Automated ICD-10 Coding
Issued Date
2025-01-01
Resource Type
Scopus ID
2-s2.0-105032462515
Journal Title
Jcsse 2025 22nd International Joint Conference on Computer Science and Software Engineering
Start Page
156
End Page
163
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SCOPUS
Bibliographic Citation
Jcsse 2025 22nd International Joint Conference on Computer Science and Software Engineering (2025) , 156-163
Suggested Citation
Akkhawatthanakun K., Narupiyakul L., Wongpatikaseree K., Hnoohom N., Muneesawang P. Pseudo-Relevance Feedback with Deep Learning for Automated ICD-10 Coding. Jcsse 2025 22nd International Joint Conference on Computer Science and Software Engineering (2025) , 156-163. 163. doi:10.1109/JCSSE67377.2025.11297923 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115770
Title
Pseudo-Relevance Feedback with Deep Learning for Automated ICD-10 Coding
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Corresponding Author(s)
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Abstract
Thailand's decentralized healthcare system faces ongoing difficulties with ICD-10 coding, including staffing shortages and coding errors that affect reimbursement processes and clinical operations. We developed a deep learning framework that combines pseudo-relevance feedback (PRF) with the Rocchio algorithm to improve how clinical notes are mapped to ICD10 codes. Our approach was tested using the MIMIC-IV-Note dataset, where we achieved a micro F1-score of 59.5 and Precision@8 of 70.8 after applying iterative PRF improvements. We also built a web-based tool that provides real-time coding predictions through an easy-to-use interface, automated data processing, and API connections that work with existing hospital systems. The results demonstrate that our approach provides hospitals with an effective tool for more accurate medical record coding while lightening the workload for clinical staff. Because the system integrates smoothly with existing healthcare IT systems, it presents a realistic option for enhancing coding efficiency in actual clinical environments.