Improving Depression Detection through Biomedical Entity Linking: A Hybrid Approach Using Embedding Models and Full-Text Search

Abstract Depression is a multifaceted mental health disorder that necessitates accurate identification of symptoms, treatments, and comorbidities for effective diagnosis and treatment planning. This paper introduces a hybrid approach to Biomedical Entity Linking (BEL) for depression detection by combining full-text search and advanced natural language processing (NLP) techniques using vector embedding models. We leveraged models like BioBERT, BioWordVec, BlueBERT, FastText, MetaMap, and Llama to improve the linking of depression-related entities in un- structured clinical texts to structured knowledge bases such as the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and Unified Medical Language System (UMLS). Additionally, we propose a novel Depression Entity Relevance Ranker (DERR), which com- bines Token Set Ratio, Jaro-Winkler Similarity, and cosine similarity of embeddings to ensure accurate ranking of entities by contextual relevance. This hybrid approach ad- dresses ambiguities and variations in depression-related terminology, significantly enhancing the accuracy of entity linking. The system achieved an overall accuracy of 84%, with a Mean Reciprocal Rank (MRR) of 0.92 and Hits@5 of 95%, demonstrating its practical value for clinical decision support systems and mental health research.