ICD-10 - ORPHA: An Interactive Complex Network Model for Brazilian Rare Diseases

Introduction

Rare diseases are conditions with very low prevalence, affecting around 400 million people globally, including 15 million in Brazil. This growing number has made rare diseases a public health priority for the World Health Organization and the Brazilian Health Ministry. Recognizing their impact, Brazil launched the national policy for rare diseases in 2014 through Ordinance nº199, aiming to ensure better access, diagnosis, and care within the public health system.

Public Health Priority

The national policy emphasizes reducing mortality and improving the quality of life of rare disease patients. Brazil’s approach reflects a global recognition of rare diseases as a public health issue. By creating guidelines and defining responsibilities, this policy seeks to integrate specialized services into the public health system, ensuring timely diagnosis, multidisciplinary treatment, and greater equity in healthcare for individuals with rare conditions across different regions of the country.

Classification Systems

Rare diseases are identified using the International Classification of Diseases 10th Revision (ICD-10), a standard global framework for coding and diagnosing diseases. While ICD-10 is widely used, its categories may be too broad for certain rare conditions. Alternative classification systems like Orphanet’s ORPHAcode offer more precise descriptions. Each system has unique advantages, and their combined use can enhance diagnostic accuracy and consistency across various healthcare and research settings.

Need for Interoperability

Healthcare data interoperability is critical when dealing with rare diseases because multiple classification systems exist. Mapping relationships between ICD-10 and ORPHAcode can improve semantic consistency and enable smoother data sharing across platforms. This facilitates better patient registries, epidemiological studies, and international collaborations. Establishing these links reduces ambiguity, supports research initiatives, and helps public health officials allocate resources effectively for prevention, diagnosis, and treatment of rare diseases.

Complex Network Model

This research proposes a complex network model to map relationships between various terminologies. By representing diseases and their codes as interconnected nodes, the model reveals gaps, overlaps, and redundancies in classification systems. Such insights can guide policymakers and researchers in refining terminologies, optimizing diagnosis protocols, and designing robust biomedical ontologies to support rare disease management, ultimately creating a more inclusive and efficient healthcare framework for affected populations.

Biomedical Ontologies

Biomedical ontologies play a vital role in unifying multiple disease terminologies. They provide structured, machine-readable relationships that support clinical decision-making and research applications. By integrating ICD-10 and ORPHAcode through ontology frameworks, it becomes possible to ensure more precise data analysis, improve interoperability, and facilitate tasks like automated coding, clinical research, and AI-driven diagnostics, thereby enhancing the overall management of rare diseases in Brazil and beyond.

Conclusion

There is no single perfect nomenclature for defining rare diseases, but each has strengths for specific contexts. Brazil’s initiative and the proposed complex network model highlight the importance of mapping relationships among terminologies. This approach fosters interoperability, supports public health decisions, and empowers clinicians and researchers. Continued refinement of classification systems and semantic networks will improve early diagnosis, treatment accessibility, and the quality of life for patients with rare diseases.

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