Context-Driven Need Detection in Home Care: A Hybrid Approach Leveraging BERT, OWL, and MEBN for Enhanced Personalized Support

Abstract

This paper introduces a novel framework for context-driven need detection in home care, with a strong emphasis on the role of Bidirectional Encoder Representations from Transformers (BERT) in providing nuanced contextual understanding. Our approach leverages BERT to process unstructured textual communications, which then guides probabilistic reasoning through the OwlMEBN Jena API, while dynamically enriching a knowledge graph represented using the Web Ontology Language (OWL). This process integrates multi-modal data via an adaptive weighted fusion of wearable sensor data, caregiver observations, and textual communications. Rigorous experiments, in a simulated environment utilizing real-world data from the e-SAAD platform, demonstrate a significant performance improvement, achieving an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.93 and an F1-score of 0.87, significantly outperforming baseline methods. This enhanced performance underscores BERT’s crucial role in enabling more accurate and personalized home care services by providing deep contextualization of beneficiary needs, while the OwlMEBN framework manages the uncertainty and provides structured probabilistic inferences.