Graph Attention Dialog Network-Based Drug Recommendation Model for Next-Gen Healthcare and Consumer-Centric Devices

The principal drug recommendation algorithms are now founded on historical electronic health records. This data insufficiently represents patients’ present health situation and neglects their immediate health requirements, leading to diminished suggestion efficacy. This work introduces a pharmacological recommendation algorithm that integrates online discussions and disease data to address this issue. This approach utilizes a discussion framework and a graph attention network centered on a patient-oriented device. Grey relational analysis integrates graph attention networks to construct connections between nodes. A novel association-aware graph structure is introduced to address the limitations of traditional graph networks in recording node associations. The advanced graph attention network develops a hierarchical dialogue encoder that encodes utterances and dialogue representations. Two categories of relational graph structures are created to illustrate discourse frameworks that incorporate contextual semantics and to comprehend the adjacency relationships among nodes. It utilizes discourse representations to improve drug prediction and recommendation, leveraging knowledge graphs and advanced graph network learning for disease representations. The F1 and Jaccard scores of the proposed technique improved by 1.8% and 3.5%, respectively, in comparison to the leading baseline DDN. This indicates that the algorithm can effectively enhance suggestion efficacy.