In contemporary healthcare, to protect patient data, electronic health records have become invaluable repositories, creating vast opportunities to leverage deep learning techniques for predictive analysis. Retinal fundus images, cirrhosis stages, and heart disease diagnostic predictions have shown promising results through the integration of deep learning techniques for classifying diverse datasets. This study proposes a novel deep learning predictive analysis framework for classifying multiple datasets by pre-processing data from three distinct sources. A hybrid deep learning model combining Residual Networks and Artificial Neural Networks is proposed to detect acute and chronic diseases such as heart diseases, cirrhosis, and retinal conditions, outperforming existing models. Dataset preparation involves aspects such as categorical data transformation, dimensionality reduction, and missing data synthesis. Feature extraction is effectively performed using scaler transformation for categorical datasets and ResNet architecture for image datasets. The resulting features are integrated into a unified classification model. Rigorous experimentation and evaluation resulted in high accuracies of 93%, 99%, and 95% for retinal fundus images, cirrhosis stages, and heart disease diagnostic predictions, respectively. The efficacy of the proposed method is demonstrated through a detailed analysis of F1-score, precision, and recall metrics. This study offers a comprehensive exploration of methodologies and experiments, providing in-depth knowledge of deep learning predictive analysis in electronic health records.

翻译：在当代医疗保健领域，为保护患者数据，电子健康记录已成为宝贵的资料库，为利用深度学习技术进行预测分析创造了巨大机遇。通过整合深度学习技术对多样化数据集进行分类，视网膜眼底图像、肝硬化分期及心脏病诊断预测已展现出有前景的结果。本研究提出了一种新颖的深度学习预测分析框架，通过对三个独立来源的数据进行预处理，实现对多数据集的分类。该框架提出了一种结合残差网络与人工神经网络的混合深度学习模型，用于检测心脏病、肝硬化及视网膜病变等急慢性疾病，其性能优于现有模型。数据集准备涉及分类数据转换、降维及缺失数据合成等方面。特征提取通过标量变换（针对分类数据集）和ResNet架构（针对图像数据集）有效实现。所得特征被整合到统一的分类模型中。经过严格实验与评估，在视网膜眼底图像、肝硬化分期和心脏病诊断预测任务中分别获得了93%、99%和95%的高准确率。通过对F1分数、精确率和召回率指标的详细分析，验证了所提方法的有效性。本研究对方法论和实验进行了全面探讨，为电子健康记录中的深度学习预测分析提供了深入见解。