Artificial intelligence (AI) and natural language processing (NLP) have found a highly promising application in automated clinical coding (ACC), an innovation that will have profound impacts on the clinical coding industry, billing and revenue management, and potentially clinical care itself. Dong et al. recently analyzed the technical challenges of ACC and proposed future directions. Primary challenges for ACC exist at the technological and implementation levels; clinical documents are redundant and complex, code sets like the ICD-10 are rapidly evolving, training sets are not comprehensive of codes, and ACC models have yet to fully capture the logic and rules of coding decisions. Next steps include interdisciplinary collaboration with clinical coders, accessibility and transparency of datasets, and tailoring models to specific use cases.
Implementation and demo of explainable coding of clinical notes with Hierarchical Label-wise Attention Networks (HLAN) - acadTags/Explainable-Automated-Medical-Coding
M. Falis, H. Dong, A. Birch, und B. Alex. Proceedings of the 21st Workshop on Biomedical Language Processing, Seite 389--401. Dublin, Ireland, Association for Computational Linguistics, (Mai 2022)
M. Falis, H. Dong, A. Birch, und B. Alex. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Seite 907--912. Online and Punta Cana, Dominican Republic, Association for Computational Linguistics, (November 2021)
H. Dong, V. Suárez-Paniagua, W. Whiteley, und H. Wu. (2020)cite arxiv:2010.15728Comment: Structured abstract in full text, 17 pages, 5 figures, 4 supplementary materials (3 extra pages), submitted to Journal of Biomedical Informatics.