Revolutionizing clinical decision making through deep learning and topic modeling for pathway optimization.

ABSTRACT: Optimizing clinical pathways is pivotal for enhancing healthcare delivery, yet traditional methods are increasingly insufficient in the face of complex, personalized medical demands. This paper introduces an innovative optimization framework that fuses Latent Dirichlet Allocation (LDA) topic modeling with Bidirectional Long Short-Term Memory (BiLSTM) networks to address the complexities of modern healthcare. The LDA component elucidates key diagnostic and treatment patterns from clinical narratives, while the BiLSTM network adeptly captures the temporal progression of patient care. Our model was validated against a real-world medical dataset, achieving remarkable results with an accuracy of over 90%, precision exceeding 28% improvement, recall with a 21% enhancement, and an F1 score that r

SUBMITTER: Tianzhao L 

PROVIDER: S-EPMC12328816 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature