目标:现实世界的数据包括人口多样性,能够深入了解老年人的慢性病死亡风险。深度学习擅长大型数据集,为现实世界的数据提供承诺。然而,目前的模型关注单一疾病,忽视患者普遍存在的合并症。此外,与疾病相比,死亡率很少见,造成极端的阶级不平衡,阻碍可靠的预测。我们的目标是开发一个深度学习框架,通过解决合并症和阶级失衡,从现实数据中准确预测死亡风险。
方法:我们集成了多任务和成本敏感的学习,开发增强的深度神经网络架构,扩展多任务学习来预测多种慢性疾病的死亡风险。每个患有慢性病的患者队列被分配到一个单独的任务,共享的低级参数通过不同的顶级网络捕获疾病间的复杂性。纳入了成本敏感函数,以确保学习每个任务的积极类别特征,并实现对多种慢性疾病死亡风险的准确预测。
结果:我们的研究涵盖了15种流行的慢性病,并对深圳482,145名患者(包括9,516例死亡)的真实数据进行了实验。中国。将提出的模型与六个模型进行比较,其中包括三个机器学习模型:逻辑回归,XGBoost,和CatBoost,和三个最先进的深度学习模型:1D-CNN,TabNet,和圣。实验结果表明,与其他比较算法相比,MTL-CSDNN在测试集上具有更好的预测结果(ACC=0.99,REC=0.99,PRAUC=0.97,MCC=0.98,G-means=0.98)。
结论:我们的方法为利用现实世界数据进行精确的多疾病死亡风险预测提供了有价值的见解。在优化慢性病管理方面提供潜在的应用,增进福祉,降低老年人的医疗成本。
OBJECTIVE: Real-world data encompass population diversity, enabling insights into chronic disease mortality risk among the elderly. Deep learning excels on large datasets, offering promise for real-world data. However, current models focus on single diseases, neglecting comorbidities prevalent in patients. Moreover, mortality is infrequent compared to illness, causing extreme class imbalance that impedes reliable prediction. We aim to develop a deep learning framework that accurately forecasts mortality risk from real-world data by addressing comorbidities and class imbalance.
METHODS: We integrated multi-task and cost-sensitive learning, developing an enhanced deep neural network architecture that extends multi-task learning to predict mortality risk across multiple chronic diseases. Each patient cohort with a chronic disease was assigned to a separate task, with shared lower-level parameters capturing inter-disease complexities through distinct top-level networks. Cost-sensitive functions were incorporated to ensure learning of positive class characteristics for each task and achieve accurate prediction of the risk of death from multiple chronic diseases.
RESULTS: Our study covers 15 prevalent chronic diseases and is experimented with real-world data from 482,145 patients (including 9,516 deaths) in Shenzhen, China. The proposed model is compared with six models including three machine learning models: logistic regression, XGBoost, and CatBoost, and three state-of-the-art deep learning models: 1D-CNN, TabNet, and Saint. The experimental results show that, compared with the other compared algorithms, MTL-CSDNN has better prediction results on the test set (ACC=0.99, REC=0.99, PRAUC=0.97, MCC=0.98, G-means = 0.98).
CONCLUSIONS: Our method provides valuable insights into leveraging real-world data for precise multi-disease mortality risk prediction, offering potential applications in optimizing chronic disease management, enhancing well-being, and reducing healthcare costs for the elderly population.