Brazilian Neural Approaches for Automated Assignment of ICD-10 Codes from Portuguese-Language Clinical Narratives

Manual assignment of ICD-10 codes from clinical narratives remains a vital yet labor-intensive task for healthcare systems worldwide. While deep learning models have advanced the automation of this process—particularly in English-language datasets—less attention has been given to the linguistic and philosophical dimensions of multilingual clinical NLP. This paper presents a comparative study of machine learning and neural models (Logistic Regression, CNN, GRU, and CNN with per-label attention) applied to Brazilian-Portuguese clinical narratives. In doing so, we introduce a document concatenation strategy to address sparse-text limitations and demonstrate that attention-based models outperform classical baselines. Beyond quantitative improvements, this study integrates philosophical insights drawn from Asavika Sciences—a transdisciplinary framework rooted in compassion, purpose, and systemic intelligence—and incorporates principles from the “New Philosophy for Health,” including the Sense of Life, the Sense of Death, and the concept of Infinitando (infinite unfolding). These perspectives reframe automated coding not merely as classification, but as a symbolic act of honoring human experience in digital systems. Additionally, we conduct a qualitative and quantitative comparison between English and Brazilian Portuguese, revealing that, when adequately modeled, the latter offers superior semantic clarity and context resolution in attention-based NLP systems. This is attributed to its morphological richness and emotional nuance, which make it especially suited for humanized health informatics. Our results affirm the feasibility and ethical promise of linguistically-aware, philosophically-aligned AI in global healthcare environments.