International Journal of Medicine and Data

This study delves into the integration of electronic health record (EHR) and machine learning techniques in the field of depression prediction and diagnosis. EHR data provides crucial clues for depression identification, while machine learning techniques can precisely extract key features from these clues, thus significantly improving the accuracy of prediction. Although researchers have successfully developed a variety of depression recognition models, they still inevitably face challenges in terms of variable data quality and privacy protection. To address these challenges, key issues such as data sparsity, category imbalance, and feature selection and extraction are discussed in detail in this paper, and corresponding solutions are proposed. On this basis, this study makes full use of EHR datasets and deep learning techniques to successfully construct a depression prediction and diagnosis model. The model not only achieves a prediction accuracy of 85%, but also a diagnosis accuracy of 90%, providing personalised treatment plans for depressed patients. This study fully demonstrates the great potential of electronic health records and machine learning in depression prediction and diagnosis, and provides strong support and reference for future related research.

Electronic health record; Machine learning; Depression