Abstract:
Autoimmune diseases represent a growing health concern globally, significantly impacting
millions of people with chronic, life-threatening disabilities. These diseases
trigger the immune system to attack healthy tissues and require the integration of
vast amounts of complex medical information, including symptoms, treatments, and
patient outcomes for effective diagnosis and treatment. However, the large volume and
diversity of data, often presented in unstructured formats such as medical reports and
research articles, provide challenges in extracting significant insights. The proposed
approach for addressing this challenge is a knowledge graph (KG), which facilitates a
structured presentation of medical knowledge. By capturing the semantic and contextual
relationships between various medical entities, such as symptoms, diagnostic tests,
and treatments, a KG can enhance the understanding and management of autoimmune
diseases. A primary challenge in creating a Knowledge Graph (KG) is to guarantee the
precise alignment of medical entities considering the extensive contextual and semantic
information linked to each entity. Ensuring entity alignment is crucial for constructing
an accurate and relevant knowledge graph in medical data, especially in autoimmune
disorders, which exhibit variances in terminology and context. For example, a symptom
such as "fatigue" can be associated with several autoimmune conditions, requiring
accurate identification and alignment. This research work focuses on aligning entities
from diverse sources such as PubMed articles, biomedical websites, and patient discharge
summaries. By utilizing both contextual and semantic information, the entity
alignment process enhances the accuracy and completeness of the knowledge graph, ensuring
that related concepts are correctly linked. The results of the alignment process
were promising, with hit@1 = 0.8823, hit@10 = 0.9090, and MRR = 0.8917, indicating
strong performance in mapping relevant entities. This research presents a knowledge
graph that integrates both contextual and semantic information through accurate entity
alignment, offering a structured framework for advancing clinical decision support
systems (CDSS) and improving the diagnosis and treatment of autoimmune diseases
