International Journal of Medicine and Data
International Journal of Medicine and Data. 2024; 8: (2) ; 10.12208/j.ijmd.20240022 .
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南京信息工程大学 江苏南京
*通讯作者： 刘丹,单位：南京信息工程大学 江苏南京；
本研究深入探讨了电子健康记录（EHR）与机器学习技术在抑郁症预测与诊断领域的融合应用。EHR数据为抑郁症的识别提供了至关重要的线索，而机器学习技术则能够精准地提取这些线索中的关键特征，从而显著提升预测的准确性。尽管研究人员已经成功开发出多种抑郁症识别模型，但仍不可避免地面临数据质量参差不齐和隐私保护等方面的挑战。针对这些挑战，本文详细讨论了数据稀疏性、类别不平衡性以及特征选择与提取等关键问题，并提出了相应的解决方案。在此基础上，本研究充分利用EHR数据集和深度学习技术，成功构建了一个抑郁症预测与诊断模型。该模型不仅预测准确率达到了85%，诊断准确率更是高达90%，为抑郁症患者提供了个性化的治疗方案。本研究充分展示了电子健康记录与机器学习在抑郁症预测与诊断中的巨大潜力，为未来的相关研究提供了有力的支持和参考。
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.
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