METHOD OF COMBINING CONTEXTUAL EMBBEDINGS WITH A VECTOR REPRESENTATION OF THE MEDICAL DOMAIN

Abstract

Navigating the intricate logical connections within clinical narratives—a medical natural language inference task—is paramount for advancing applications like AI-assisted clinical decision-making and the automated interpretation of patient records. However, mastering this domain is particularly arduous due to the specialized lexicon, complex conceptual relationships, and subtle semantic variations inherent in medical texts. This research introduces an innovative methodology to elevate medical natural language inference performance by effectively combining structured, field-specific knowledge with insights gleaned from textual sentiment. Our approach capitalizes on MultE, a cutting-edge algorithm for embedding knowledge graphs, to distill profound semantic relationships from the Unified Medical Language System (UMLS). These distilled knowledge representations are then amalgamated with contextual word embeddings generated by BioELMo. To further enrich contextual understanding, sentiment data pertinent to the medical field, extracted via MetaMap, is also integrated. The system architecture processes this composite feature set—BioELMo embeddings augmented by domain knowledge and sentiment vectors—through a bidirectional Long Short-Term Memory (BiLSTM) network, which is subsequently enhanced by an attention mechanism that dynamically assigns importance to different input segments. Validation on the MedNLI benchmark dataset, featuring 14,049 expert-labeled premise-hypothesis pairs, revealed exceptional efficacy. The proposed system achieved 81.14% accuracy, 79.62% recall, an F1-score of 79.85%, and an AUC-ROC of 85.06%, surpassing established baseline techniques. These accomplishments underscore that the deliberate incorporation of specialized knowledge and sentiment cues can dramatically boost natural language inference capabilities in the medical arena, thereby providing a sturdy platform for engineering more dependable and intelligent healthcare solutions.