Explainable Bilingual Medical-Question-Answering Model Using Ensemble Learning Technique

Accessing reliable medical information is a major challenge for healthcare professionals due to limited accessibility to real-time medical data sources. The study’s objectives are maximization of response accuracy with minimal latency and enhancement of the model’s interpretability. An explainable bilingual medical-question-answering system (MQAS) is introduced to improve accessibility and trust in healthcare information retrieval. Using knowledge-aware networks (KANs), retrieval augmented generation (RAG), and linked open data (LOD), a synthetic bilingual dataset is generated. Through the application of a synthetic dataset and Bayesian optimization HyperBand (BOHB)-based hyperparameter optimization, the performance of GPT-Neo and RoBERTa models is fine-tuned. The outcomes of GPT-Neo and RoBERTa are ensembled using the weighted majority voting approach, while Shapley Additive ExPlanation (SHAP) value provides interpretability and transparency. The proposed model is trained and evaluated using diverse medical-question-answering datasets, demonstrating superior performance over baseline models. It achieves a generalization accuracy of 90.58%, an F1-score of 89.62%, and a BLEU score of 0.80 with a low inference time of 3.4 seconds per query. The findings underscore the model’s potential in delivering accurate, bilingual, and explainable medical responses. This study establishes a foundation for building multilingual healthcare information systems, promoting inclusive and equitable access to medical information.