Leveraging deep neural network and language models for predicting long-term hospitalization risk in schizophrenia.

ABSTRACT: Early warning of long-term hospitalization in schizophrenia (SCZ) patients at the time of admission is crucial for effective resource allocation and individual treatment planning. In this study, we developed a deep learning model that integrates demographic, behavioral, and blood test data from admission to forecast extended hospital stays using a retrospective cohort. By utilizing language models, our developed algorithm efficiently extracts 95% of the unstructured electronic health records data needed for this work, while ensuring data privacy and low error rate. This paradigm has also been demonstrated to have significant advantages in reducing potential discrimination and erroneous dependencies. By utilizing multimodal features, our deep learning model achieved a classification accurac

SUBMITTER: Bao Y 

PROVIDER: S-EPMC11882783 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature