Bao Yihang, Wang Wanying, Liu Zhe, Wang Weidi, Zhao Xue, Yu Shunying, Lin Guan Ning
Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China.
Schizophrenia (Heidelb). 2025 Mar 5;11(1):35. doi: 10.1038/s41537-025-00585-2.
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 accuracy of 0.81 and an AUC of 0.9. Key risk factors identified included advanced age, longer disease duration, and blood markers such as elevated neutrophil-to-lymphocyte ratio, lower lymphocyte percentage, and reduced albumin levels, validated through comprehensive interpretability analyses and ablation studies. The inclusion of multimodal data significantly improved prediction performance, with demographic variables alone achieving an accuracy of 0.73, which increased to 0.81 with the addition of behavioral and blood test data. Our approach outperformed traditional machine learning methods, which were less effective in predicting long-term stays. This study demonstrates the potential of integrating diverse data types for enhanced predictive accuracy in mental health care, providing a robust framework for early intervention and personalized treatment in SCZ management.
精神分裂症（SCZ）患者入院时的长期住院早期预警对于有效资源分配和个体化治疗规划至关重要。在本研究中，我们开发了一种深度学习模型，该模型整合了入院时的人口统计学、行为学和血液检测数据，以回顾性队列预测延长的住院时间。通过利用语言模型，我们开发的算法有效地提取了这项工作所需的95%的非结构化电子健康记录数据，同时确保了数据隐私和低错误率。这种范式还被证明在减少潜在歧视和错误依赖方面具有显著优势。通过利用多模态特征，我们的深度学习模型实现了0.81的分类准确率和0.9的AUC。确定的关键风险因素包括高龄、更长的病程以及血液标志物，如中性粒细胞与淋巴细胞比值升高、淋巴细胞百分比降低和白蛋白水平降低，通过全面的可解释性分析和消融研究得到了验证。多模态数据的纳入显著提高了预测性能，仅人口统计学变量的准确率为0.73，加入行为学和血液检测数据后提高到了0.81。我们的方法优于传统机器学习方法，传统方法在预测长期住院方面效果较差。本研究证明了整合多种数据类型以提高精神卫生保健预测准确性的潜力，为SCZ管理中的早期干预和个性化治疗提供了一个强大的框架。
