MMAC-Net: A Multi-Modal Multi-Label Attention-Based Deep Learning Approach for Automated ICD-9 Coding of Rare Disease Admissions from Electronic Health Records

Automating the identification of International Classification of Diseases (ICD) codes from electronic health records (EHRs) presents a critical challenge, particularly for rare diseases where existing computational methods severely underperform due to extreme long-tail label distributions. To address this, we propose a multi-modal deep learning framework known as MMAC-Net, designed to enhance the retrospective assignment of ICD-9 codes to admissions involving rare pathologies. The model integrates unstructured clinical narratives with structured auxiliary data, specifically pharmacological prescriptions and microbiology events, using a convolutional attention-based architecture. Through a late fusion mechanism, it synthesizes attention-weighted textual representations with dense embeddings of the structured data types. Validation on the MIMIC-III dataset shows consistent improvements over a matched text-only baseline evaluated under an identical protocol. On the full dataset of 8930 ICD codes, the framework achieved a Micro-AUC of 0.997 and Precision@8 of 0.875. On the subset of admissions carrying at least 1 of 568 rare codes, adding the two structured modalities to the text encoder raises Macro-F1 from 0.011 to 0.084 and Micro-F1 from 0.368 to 0.513 relative to the text-only baseline, corresponding to relative increases of 6.69 and 0.39, respectively, while Precision@8 rises from 0.092 to 0.159 and Micro-AUC from 0.966 to 0.985. While extreme class imbalance remains a formidable obstacle, these findings underscore that incorporating structured clinical context partially mitigates the limitations of purely natural language processing approaches. Practically, the framework is intended as a decision-support component that presents a ranked shortlist of candidate codes to a human coder or clinician; by recovering rare codes that text-only systems miss, it targets the under-coding of low-prevalence conditions that degrades registry completeness and downstream epidemiological estimates.