Psychological Health Prediction Based on the Fusion of Structured and Unstructured Data in EHR: a Case Study of Low-Income Populations

In view of the high incidence and complexity of mental health problems among low-income people, existing studies have mostly relied on structured data in electronic health records (EHR), ignoring the potential information contained in rich unstructured data. In order to effectively predict the mental health status of low-income people, this study cites a structured and unstructured data fusion model based on the most advanced deep learning technology. First, the BERT (Bidirectional Encoder Representations from Transformers) model is used to perform semantic understanding and feature extraction on the unstructured text data in EHR. Next, TabTransformer (Transformer-based Model for Tabular Data) is used to efficiently encode structured data and capture the complex relationships between data. Finally, through the multimodal fusion mechanism, structured and unstructured features are deeply integrated to form a comprehensive feature representation. In the experimental conclusion, the fusion model shows significant improvements in evaluation metrics such as accuracy, with accuracy increasing from 81.3% of the benchmark model to 85.2%. In addition, the cross-dataset generalization ability test shows that the model maintains good performance stability between different data sources. In the above conclusions, this study demonstrates the effectiveness of the fusion of structured and unstructured data in improving the accuracy of mental health prediction for low-income people, providing strong support for future precision medical interventions.